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I am a researcher in the Department of Civil and Environmental Engineering.
Our research focuses on the investigation of how various contaminants affect
the ground water quality and how we could design remediation systems. An
important approach we are using for this type of investigation is modeling
contaminant fate and transport in the subsurface on computers. The resources
provided by Tufts Cluster Center are very important to us. Our simulations
usually take days or even weeks on a single CPU. The clusters can either
expedite each simulation if we use simulators that enable parallel
computing, or allow us to simulate multiple serial processes simultaneously.
The significant improvement in computing efficiency is critical for us to
commit work quality to funding sponsors. We expect that our work will
improve the cuurent understanding of contamination in the subsurface,
provide cutting-edge assessment tools, and stimulate innovative treatment
technologies.
Eric Miller
Our work concerns the development of tomographic processing methods for environmental remediation problems. Specifically, we are interested in using electrical resistance tomography (ERT) to estimate the geometry of regions of the subsurface contaminated by chemicals such as TCE or PCE. Though the concept of ERT is not unlike the more familiar computed axial tomography (CAT) used for medical imaging, the physics of ERT are a bit more complicated thereby leading to computationally intensive methods for turning data into pictures. Luckily these computational issues are, at a high level, easily parallelizable. Thus, we have turned to Star-P as the tool of choice for the rapid synthesis of our algorithms.
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My research for my MS in Mechanical Engineering is based on using the
software available on the cluster to model a cold forging process. This
model, paired with experimental data, will then be used to develop a tool to
predict forging work piece cracking. The tool will provide a manufacturer
of airfoils for use in the aircraft engine industry a method to rapidly
develop new processing while avoiding costly physical trials.
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