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CHPC - Research Computing Support for the University

In addition to deploying and operating high performance computational resources and providing advanced user support and training, CHPC serves as an expert team to broadly support the increasingly diverse research computing needs on campus. These needs include support for big data, big data movement, data analytics, security, virtual machines, Windows science application servers, protected environments for data mining and analysis of protected health information, and advanced networking. Visit our Getting Started page for more information.

Understanding Wind Energy

By Gerard Cortina, Wind Energy & Turbulence, Department of Mechanical Engineering

The Wind Energy and Turbulence laboratory was designed to improve the current understanding of wind energy harvesting. To achieve this goal we dedicate much of our efforts to develop new knowledge on the turbulent atmospheric boundary layer. Our focus resides on solving high resolution numerical simulations with the help of the Center for High Performance Computing at the university of Utah, which we ultimately complement with the analysis of experimental data.

Currently we mainly use Large Eddy Simulations, which are capable of resolving most of the atmospheric turbulent scales as well as the wind turbines, providing very good results when compared to the experimental data. We are highly interested in improving the current conception of the land-atmosphere energy exchanges, and our work strives to fill the gaps of our current understanding. It is only by properly capturing the land-atmosphere connection that forces the atmospheric flow aloft that we will be able to reproduce with high accuracy the atmospheric flow.

System Status

last update: 09/20/17 5:43 pm
General Nodes
system procs % util.
ember 936/984 95.12%
kingspeak 860/860 100%
lonepeak 1020/1028 99.22%
Restricted Nodes
system procs % util.
ash 7400/7448 99.36%
apexarch Status Unavailable
ember 1316/1316 100%
kingspeak 7020/7036 99.77%
lonepeak 340/400 85%

Cluster Utilization

Last Updated: 9/19/17