Californian and Swiss researchers have been using the Kraken supercomputer to model what would happen if a major earthquake hit the southern portion of the San Andreas Fault. The entire fault extends more than 800 miles, from San Francisco to Southern California. What makes these researchers’ work different from previous studies is that they’ve factored
Supernovae exhibit the most-energetic explosions, dispersing elements that make life possible into the universe. However, the energy source for the violent death of these massive stars is not known. Researchers using UT’s Kraken supercomputer have created three-dimensional simulations that have made great strides in uncovering the source.
Severe weather raises questions about the phenomena that cause it. The answer to all questions is atmospheric conditions. The atmosphere consists of varying layers of gases or fluid structures. Researchers at the National Institute for Computational Sciences are using the supercomputing power of UT’s Kraken to model how the structures interact to help prepare accurate
Jacek Jakowski, a computational scientist at the National Institute for Computational Sciences, was interviewed on an HPCWire podcast about a new computational capability he and his team developed to study the dynamics of prospective energy materials under diverse environmental situations. The researcher discussed how he and his team are using the Kraken supercomputer to explore
Research being done on the supercomputer Kraken holds promise for overcoming limitations in the study of energy and materials applications. The method employs quantum mechanics to understand how nuclear effects change the dynamics of microscopic-size materials.
A team of four UT students along with a student from Hardin Valley Academy and Oak Ridge High School are heading to Denver to compete in the SC!13’s international student supercomputing cluster competition. The competition is designed to introduce the next generation of students to the high powered computing community.
The National Science Foundation’s “News from the Field” and Inside HPC featured work done at then National Institute for Computational Sciences. The work is looking into using cellulase enzymes in the biomass in industrial processes to make biofuels. To read the full story, visit NSF’s website and Inside HPC‘s website.
Cellulase enzymes found in nature from sources such as wood-degrading fungi or in cows’ stomach compartments form one of the key catalysts for breaking down plant biomass to make biofuels. But, they remain quite expensive. Compute allocations from the Extreme Science and Engineering Discovery Environment (XSEDE) have made a breakthrough possible that could have big
Researchers using the supercomputing resources at the National Institute for Computational Sciences are investigating a way to recommend sources for users at university libraries. The result would be similar to the “recommender system” at Amazon.com which prioritizes descriptive information based on social behavior.
Tornado forecasting remains a persistent challenge. Researchers using supercomputers at the National Institute for Computational Sciences are trying to change this. Modest hardware enables researchers to simulate a supercell, said the researchers, but supercomputers can run at a high enough resolution to properly capture tiny features associated with the tornado itself.
The Earth has a shield which can protect it from damaging solar particles. However, this shield can be infiltrated and the result can be a disruption of power grids and communications networks, and radiation on Earth. Researchers using supercomputers at the National Institute for Computational Sciences are creating a topological map of Earth’s magnetosphere, allowing
As disease progresses over space and time in the body, high-resolution imaging can capture the changes taking place down to the sub-cellular level; meanwhile, huge sets of hereditary (genomic) information hold clues about the dynamics of illness. Comparing certain characteristics in the images with genomic and clinical data may be key in predicting disease progression