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January 1993
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REU Program Gives Graduate Research Experience to Undergraduates
With support from the National Science Foundation, the 1993 Research
Experience for Undergraduates (REU) program at Syracuse gave 15 students
the chance to work with Syracuse faculty for ten weeks this summer on
computational science and engineering projects. As a true team effort,
the REU program brought together faculty, staff, and graduate students
from Syracuse and the CRPC to work with undergraduate students on areas
such as software engineering, algorithm development, virtual reality,
and applications in physics and engineering.
The program had two main components: an intensive training course and an
individual research project. The training included lectures on numerical
methods, computer architectures, and programming techniques. The
research projects gave the participants concrete research experience and
exposed them to all the activities associated with scholarly research:
writing research proposals and technical reports, giving oral
presentations, and preparing posters on the results of a project.
Students were matched up as "apprentices" with mentors in the areas of
their interest.
The following projects are a sample of the diverse areas in which the
students have pursued research:
Visualization of Flow Field Structures in Jet Engine Exhaust
Kimberly Rak, student Eric F. Spina, mentor
This project examined ways to reduce the noise produced by high-speed
jet engines. It was determined beforehand that as the exhaust from the
engine mixes with the surrounding air, the noise is reduced. The team
used visualization software to produce images of air flow produced by
four jet nozzles of varying shapes. The images were used along with
experimental data, such as pressure, velocity, and acoustical
measurements, to evaluate the best nozzle design for reduced engine
noise.
Say it Ain't So, or Parallel Genetic Algorithms for Automated Reasoning
Stephanie Weirich, student Chilukuri K. Mohan, mentor
In a well-known method of automated reasoning, a computer program is
asked to determine whether a set of statements is contradictory using
simple "inference rules" that allow the system to draw logical
conclusions. Current reasoning systems are very slow for large problems.
A genetic algorithm has been found to be useful in speeding up the
reasoning process for propositional logic problems. In the REU project,
a parallel implementation was built for this algorithm on a Thinking
Machines CM-5, with the goal of achieving large speedups in execution.
Weirich and Mohan are continuing this promising line of research.
A Model to Predict Photoreceptor Electrical Output Based on Natural
Light Input
Oren K. Nagasako, student Gustav Engbretson, Ernest Sibert,
mentors
This project proposed to model the response of a photoreceptor to
natural light. The group collected data on the responses of a
photoreceptor in a lizard to determine sensitivities to light at
different frequencies and intensities. A software program called SOLRAD
provided data on natural lighting conditions in the lizard's
environment. The combined figures were then entered into a parallel
computing model. Data from other animals were also used and the effects
of temperature on the model were studied.
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