Shyh-Kuang Ueng

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.

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

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.

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.