Degree Type


Date of Award


Degree Name

Doctor of Philosophy


Aerospace Engineering

First Advisor

R. G. Hindman

Second Advisor

J. M. Vogel

Third Advisor

L. N. Wilson


Surface definition deals with representing a surface analytically using a finite number of parameters and with acceptable levels of error. In the past few years it has become a key discipline in Computational Fluid Dynamics (CFD). Recent advances in computers and numerical algorithms have made it possible for CFD practitioners to attempt flow solutions about complex three-dimensional geometries. The first step in this process is having a numerical representation of the shape. In many cases of interest such a representation already exists; i.e., aircraft designed on a computer. Such Computer-Aided Design (CAD) descriptions do not exist, though, for objects found in nature or predating CAD. In such situations a technique for measuring the object and then constructing a surface conforming to these measurements is needed;Existing techniques for 3-D surface definition often require considerable human intervention, both in the measuring and the reconstruction process. This is a time consuming proposition. It is desirable to develop a fully automated alternative;Three-dimensional objects can be measured accurately and quickly from multiple viewpoints using a Cyberware laser digitizer. The digitizer returns the coordinates of a set of surface points. The problem is then to construct a faithful representation of the original object from these points. The algorithm proposed here has two distinct stages. In the first stage, surface fragments, using information from a single view, are produced by employing a visibility constraint and a 2-D Delaunay triangulation technique. In the next stage, surfaces from multiple views are combined through an approach that emulates the machining operation of milling. The final result is a non-convex, triangular faceted, polyhedron that approximates the object shape;A sequential version of the virtual milling algorithm exists on a Silicon Graphics workstation. The algorithm is of O(NlogN) complexity, where N is the number of data points. Experimental results have been obtained for a scaled F117-A model scanned from multiple viewpoints. Several topological issues have been addressed;A parallel version of the algorithm has been implemented on the Intel Gamma Prototype, a 128 node, distributed-memory, MIMD computer. Run times are compared to those obtained on an Iris 310/VGX workstation.



Digital Repository @ Iowa State University,

Copyright Owner

Kumaran Kalyanasundaram



Proquest ID


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File Size

104 pages