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January 1993

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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