David S. Thompson

Eduction of swirling structure using the velocity gradient tensor

We propose a technique for locating swirling regions of a flowfield. The technique is based on the eigenvalues of the velocity gradient tensor. We show that regions of swirling flow are characterized by complex eigenvalues for a constant velocity gradient tensor. Using results obtained in this analysis, we define an approximate parameter to indicate the tendency for the fluid to swirl about the point in question. The technique is illustrated by application to several two- and three-dimensional flowfields. In addition, the basic ideas contained here suggest the existence of a fluid property that we have termed intrinsic swirl

Geometric verification of swirling features in flow fields

In this paper, we present a verification algorithm for swirling features in flow fields, based on the geometry of streamlines. The features of interest in this case are vortices. Without a formal definition, existing detection algorithms lack the ability to accurately identify these features, and the current method for verifying the accuracy of their results is by human visual inspection. Our verification algorithm addresses this issue by automating the visual inspection process. It is based on identifying the swirling streamlines that surround the candidate vortex cores. We apply our algorithm to both numerically simulated and procedurally generated datasets to illustrate the efficacy of our approach.

A novel approach to vortex core region detection

In this paper we present a simple and efficient vortex core region detection algorithm based on ideas derived from combinatorial topology. These ideas originated from Sperners lemma, which by itself is of little value to detecting vortex cores. However, we take these ideas from the lemma and apply them in a point-based fashion to detecting vortex core regions. The resulting algorithms for both 2D and 3D are quite simple and very efficient compared to existing ones. We applied our algorithms to both numerically simulated and procedurally generated datasets to illustrate the efficacy of our approach.

Time Dependent Processing in a Parallel Pipeline Architecture

Pipeline architectures provide a versatile and efficient mechanism for constructing visualizations, and they have been implemented in numerous libraries and applications over the past two decades. In addition to allowing developers and users to freely combine algorithms, visualization pipelines have proven to work well when streaming data and scale well on parallel distributed- memory computers. However, current pipeline visualization frameworks have a critical flaw: they are unable to manage time varying data. As data flows through the pipeline, each algorithm has access to only a single snapshot in time of the data. This prevents the implementation of algorithms that do any temporal processing such as particle tracing; plotting over time; or interpolation, fitting, or smoothing of time series data. As data acquisition technology improves, as simulation time-integration techniques become more complex, and as simulations save less frequently and regularly, the ability to analyze the time-behavior of data becomes more important. This paper describes a modification to the traditional pipeline architecture that allows it to accommodate temporal algorithms. Furthermore, the architecture allows temporal algorithms to be used in conjunction with algorithms expecting a single time snapshot, thus simplifying software design and allowing adoption into existing pipeline frameworks. Our architecture also continues to work well in parallel distributed-memory environments. We demonstrate our architecture by modifying the popular VTK framework and exposing the functionality to the ParaView application. We use this framework to apply time-dependent algorithms on large data with a parallel cluster computer and thereby exercise a functionality that previously did not exist.

Solution adaptive grid strategies based on point redistribution

Solution adaptive grid strategies based on the redistribution of a fixed number of points are described in this paper. The redistribution is performed using weight functions that vary based on significant flow features. The weight functions are evaluated using an equidistribution principle. In this paper, emphasis is placed on the development of weight functions applicable to compressible flows exhibiting large scale separated vortical flows, vortex‐vortex and vortex‐surface interactions, separated shear layers and multiple shocks of diAerent intensities. Algebraic, elliptic and parabolic methods of grid generation have been utilized for structured grid redistribution. Additionally, a point movement scheme is presented for generalized (structured/unstructured/hybrid) grid adaptation. Computer Aided Geometry Design (CAGD) techniques are combined with redistribution schemes to maintain the fidelity of solid boundaries. In particular, solid boundaries are represented using Non-Uniform Rational B-Splines (NURBS). A grid generation software system ‐ Parallel Multiblock Adaptive Grid generation (PMAG) ‐ using an elliptic redistribution scheme is also described with emphasis placed on the parallel implementation for multiblock structured grids with unstructured blocking topologies and on interpolation issues. Computational examples demonstrating the influence of diAerent weight functions and grid redistribution strategies are presented. ” 2000 Elsevier Science S.A. All rights reserved.

Path-based control of smoke simulations

In this paper, we propose a novel path-based control method for generating realistic smoke animations. Our method allows an animator to specify a 3D curve for the smoke to follow. Path control is then achieved using a linear (closed) feedback loop to match the velocity field obtained from a 3D flow simulation with a target velocity field. The target velocity field can be generated in a variety of ways and may include the small scale swirling motion characteristic of turbulent flows. We provide several examples of complex smoke paths to demonstrate the efficacy of our approach.

Iced airfoil simulation using generalized grids

A new strategy for simulating the flow around iced airfoils is presented in this paper. Two different approaches are used to generate quality grids over the iced airfoil. Both grids can be categorized as generalized grids since multiple element types are employed in each. In the first approach, a structured grid is generated near the body using a marching scheme and the rest of the domain is filled with an unstructured grid. In the second approach, the marching strategy is coupled with a node deletion/insertion algorithm to generate a quad-dominant grid. An edge based data structure is used to store the grid information to handle polygons with an arbitrary number of sides. A finite-volume, cell-centered scheme is used to solve the integral form of the full Navier-Stokes equations. The numerical fluxes crossing the cellfaces are calculated using Roes approximate Riemann solver. The turbulent viscosity is estimated using Spalart-Allmaras one equation turbulence model. The results of computations are presented, together with comparison to NPARC results.

Physics-based feature mining for large data exploration

One effective way of exploring large scientific data sets is a process called feature mining. The two approaches described here locate specific features through algorithms that are geared to those features underlying physics. Our intent with both approaches is to exploit the physics of the problem at hand to develop highly discriminating, application-dependent feature detection algorithms and then use available data mining algorithms to classify, cluster, and categorize the identified features. We have also developed a technique for denoising feature maps that exploits spatial-scale coherence and uses what we call feature preserving wavelets. The examples presented demonstrate our feature mining approach as applied to steady computational fluid dynamics simulations on curvilinear grids.

EVITA - EFFICIENT VISUALIZATION AND INTERROGATION OF TERA-SCALE DATA

Large-scale computational simulations of physical phenomena produce data of unprecedented size (terabyte and petabyte range). Unfortunately, development of appropriate data management and visualization techniques has not kept pace with the growth in size and complexity of such datasets. To address these issues, we are developing a prototype, integrated system (EVITA) to facilitate exploration of tera-scale datasets. The cornerstone of the EVITA system is a representational scheme that allows ranked access to macroscopic features in the dataset. The data and grid are transformed using wavelet techniques while a feature-detection algorithm is used to identify and rank contextually significant features directly in the wavelet domain. The most significant parts of the dataset are thus available for detailed examination in a progressive fashion. The work presented here is similar in essence to much of the work in the traditional data-mining community. We first describe the basic system and follow with a discussion of ongoing work, focusing on efforts in multiscale feature detection and progressive access. Finally, we demonstrate the system for a two-dimensional vector field derived from an oceanographic dataset.

Vortex Visualization for Practical Engineering Applications

In order to understand complex vortical flows in large data sets, we must be able to detect and visualize vortices in an automated fashion. In this paper, we present a feature-based vortex detection and visualization technique that is appropriate for large computational fluid dynamics data sets computed on unstructured meshes. In particular, we focus on the application of this technique to visualization of the flow over a serrated wing and the flow field around a spinning missile with dithering canards. We have developed a core line extraction technique based on the observation that vortex cores coincide with local extrema in certain scalar fields. We also have developed a novel technique to handle complex vortex topology that is based on k-means clustering. These techniques facilitate visualization of vortices in simulation data that may not be optimally resolved or sampled. Results are included that highlight the strengths and weaknesses of our approach. We conclude by describing how our approach can be improved to enhance robustness and expand its range of applicability