Shyh-Kuang Ueng

Out-of-core streamline visualization on large unstructured meshes

This paper presents an out-of-core approach for interactive streamline construction on large unstructured tetrahedral meshes containing millions of elements. The out-of-core algorithm uses an octree to partition and restructure the raw data into subsets stored into disk files for fast data retrieval. A memory management policy tailored to the streamline calculations is used such that, during the streamline construction, only a very small amount of data are brought into the main memory on demand. By carefully scheduling computation and data fetching, the overhead of reading data from the disk is significantly reduced and good memory performance results. This out-of-core algorithm makes possible interactive streamline visualization of large unstructured-grid data sets on a single mid-range workstation with relatively low main-memory capacity: 5-15 megabytes. We also demonstrate that this approach is much more efficient than relying on virtual memory and operating systems paging algorithms.

Efficient streamline, streamribbon, and streamtube constructions on unstructured grids

Streamline construction is one of the most fundamental techniques for visualizing steady flow fields. Streamribbons and streamtubes are extensions for visualizing the rotation and the expansion of the flow. The paper presents efficient algorithms for constructing streamlines, streamribbons, and streamtubes on unstructured grids. A specialized Runge-Kutta method is developed to speed up the tracing of streamlines. Explicit solutions are derived for calculating the angular rotation rates of streamribbons and the radii of streamtubes. In order to simplify mathematical formulations and reduce computational costs, all calculations are carried out in the canonical coordinate system instead of the physical coordinate system. The resulting speed up in overall performance helps explore large flow fields.

A ship motion simulation system

In this article, efficient computational models for ship motions are presented. These models are used to simulate ship movements in real time. Compared with traditional approaches, our method possesses the ability to cope with different ship shapes, engines, and sea conditions without the loss of efficiency. Based on our models, we create a ship motion simulation system for both entertainment and educational applications. Our system assists users to learn the motions of a ship encountering waves, currents, and winds. Users can adjust engine powers, rudders, and other ship facilities via a graphical user interface to create their own ship models. They can also change the environment by altering wave frequencies, wave amplitudes, wave directions, currents, and winds. Therefore, numerous combinations of ships and the environment are generated and the learning becomes more amusing. In our system, a ship is treated as a rigid body floating on the sea surface. Its motions compose of 6 degrees of freedom: pitch, heave, roll, surge, sway, and yaw. These motions are divided into two categories. The first three movements are induced by sea waves, and the last three ones are caused by propellers, rudders, currents, and winds. Based on Newton’s laws and other basic physics motion models, we deduce algorithms to compute the magnitudes of the motions. Our methods can be carried out in real time and possess high fidelity. According to ship theory, the net effects of external forces on the ship hull depend on the ship shape. Therefore, the behaviors of the ship are influenced by its shape. To enhance our physics models, we classify ships into three basic types. They are flat ships, thin ships, and slender ships. Each type of ship is associated with some predefined parameters to specify their characteristics. Users can tune ship behaviors by varying the parameters even though they have only a little knowledge of ship theory.

Fast Algorithms for Visualizing Fluid Motion in Steady Flow on Unstructured Grids

The plotting of streamlines is an effective way of visualizing fluid motion in steady flows. Additional information about the flowfield, such as local rotation and expansion, can be shown by drawing in the form of a ribbon or tube. In this paper, we present efficient algorithms for the construction of streamlines, streamribbons and streamtubes on unstructured grids. A specialized version of the Runge-Kutta method has been developed to speed up the integration of particle pathes. We have also derived close-form solutions for calculating angular rotation rate and radius to construct streamribbons and streamtubes, respectively. According to our analysis and test results, these formulations are two to four times better in performance than previous numerical methods. As a large number of traces are calculated, the improved performance could be significant.

A note on a linear time algorithm for constructing adjacency graphs of 3D FEA data

In this paper, we present an algorithm for constructing adjacency graphs of 3D finite element analysis (FEA) data. Adjacency graphs are created to represent the connectivities of FEA data cells. They are used in most visualization methods for FEA data. We stress that in many engineering applications FEA data sets do not contain the adjacency information. This is opposite to computer-aided geometric design where, e.g., the winged edge geometrical representation is usually generated and utilized. By establishing intermediate data structures and using bin-sorting, we developed an efficient algorithm for constructing such graphs. The total time complexity of the algorithm is linear in the number of data cells.

An out-of-core method for computing connectivities of large unstructured meshes

Adjacency graphs of meshes are important for visualizing or compressing unstructured scientific data. However, calculating adjacency graphs requires intensive memory space. For large data sets, the calculation becomes very inefficient on desk-top computers with limited main memory. In this article, an out-of-core method is presented for finding connectivities of large unstructured FEA data sets. Our algorithm composes of three stages. At the first stage, FEA cells are read into main memory in blocks. For each cell block read, cell faces are generated and distributed into disjoint groups. These groups are small enough such that each group can reside in main memory without causing any page swapping. The resulted groups are stored in disk files. At the second stage, the face groups are fetched into main memory and processed there one after another. Adjacency graph edges are determined in each face group by sorting faces and examining consecutive faces. The edges contained in a group are kept in a disk file. At the third stage, edge files are merged into a single file by using external merge sort, and the connectivity information is computed.

Out-of-core encoding of large tetrahedral meshes

In this paper, an out-of-core data compression method is presented to encode large Finite Element Analysis (FEA) meshes. The method is comprised with two stages. At the first stage, the input FEA mesh is divided into blocks, called octants, based on an octree structure. Each octant must contain less FEA cells than a predefined limit such that it can fit into the main memory. Octants produced in the data division are stored in disk files. At the second stage, the octree is traversed to enumerate all the octants. These octants are fetched into the main memory and compressed there one by one. To compress an octant, the cell connectivities of the octant are computed. The connectivities are represented by using an adjacency graph. In the graph, a graph vertex represents an FEA cell, and if two cells are adjacent by sharing a face then an edge is drawn between the corresponding vertices of the cells. Next the adjacency graph is traversed by using a depth first search, and the mesh is split into tetrahedral strips. In a tetrahedral strip, every two consecutive cells share a face, and only one vertex reference is needed for specifying a cell. Therefore, less memory space is required for storing the mesh. According to the different situations encountered during the depth first search, the tetrahedral strips are encoded by using four types of instructions. When the traversal is completed, the tetrahedral strips are converted into a byte string and written into a disk file. To decode the compressed mesh, the instructions kept in the disk file are fetched into the main memory in blocks. For each block of instructions, the instructions are executed one by one to reconstruct the mesh. Test results reveal that the out-of-core compression method can compress large meshes on a desk-top machine with moderate memory space within reasonable time. The out-of-core method also achieves better compression ratios than an incore method which was developed in a previous research.

Data compression method for tetrahedral meshes

In this paper, a looseless compression scheme is presented for Finite Element Analysis(FEA) data. In this algorithm, all FEA cells are assumed to be tetrahedra. Therefore a cell has at most four neighboring cells. Our algorithm starts with computing the indices of the four adjacent cells for each cell. The adjacency graph is formed by representing a cell by a vertex and by drawing an edge between two cells if they are adjacent. The adjacency graph is traversed by using a depth first search, and the mesh is split into tetrahedral strips. In a tetrahedral strip, every two consecutive cells share a face, and thus only one vertex index has to be specified for defining a tetrahedron. Therefore the memory space required for storing the mesh is reduced. The tetrahedral strips are encoded by using four types of instructions and converted into a sequence of bytes. Unlike most 3D geometrical compression algorithms, vertex indices are not changed in our scheme. Rearrangement of vertex indices is not required.

An Adaptive Gauss Filtering Method

An adaptive filtering method for volume data is presented in this paper. In this filtering method, the input data set is re-sampled to create a hierarchy of multiple-level data sets. A data classification task is performed at each level of the data pyramid to decide the local structure types. Data voxels are classified as linear, planar, or blob structures, based on the gradients and the eigenvalues of Hessian matrices. The classification results are used to adjust the shapes and orientations of filters such that noises are suppressed while key features are preserved.

Multi-resolution Unsteady Flow Visualization

In this paper, a visualization method for unsteady incompressible flows is presented. Flow variations are illustrated by animating a sequence of evenly-spaced streamline images. An interpolation scheme based on special Navier-Stoke equations is utilized to interpolate flow fields in the time domain. Thus temporal resolutions are increased and smooth streamline animations are guaranteed. To create the evenly-spaced streamline images, a hierarchy of regular grids is super-imposed upon the domain. The grids are used in seeding and controlling the separating distances between streamlines. The time step size in the streamline integration is adoptively adjusted by using streamline curvatures and speeds such that streamlines will not diverge and fewer samples are computed.