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Eugene Morgan

The Tufts linux cluster allows me to work with large amounts of data within a reasonable time frame. I first used the cluster to interpolate sparse data points over a fairly large 3-dimensional space. The cluster has also dramatically sped up the calculation of semivariance for dozens of sections of seafloor containing vast numbers of data points, quickly performed thousands of Monte Carlo simulations, and computed statistics on one of the largest global wind speed datasets containing ~3.6 billion data points. I have most recently used the cluster find optimal parameters for rock physics equations using a genetic algorithm. Most of these activities have been or will be incorporated in technical publications.

Eric Thompson

We have used the Tufts Linux Cluster to further our understanding of the seismic response of near-surface soils. This behavior, often termed "site response," can often explain why locations heavily damaged by an earthquake are frequently observed adjacent to undamaged locations. Standard modeling procedures often fail to accurately model this behavior. The failure of these models is often attributed to the uncertainty of the soil properties. However, using the Tufts Linux Cluster we have shown that the underlying theoretical assumptions of the standard model (vertically incident plane SH-wave propagation through a laterally constant medium) are responsible for the failure to match the observed site response behavior.

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