In Situ processing enables full wind farm simulation by reducing file size from 234TB to under 1TB
Researchers at the University of Wyoming (UW) and Intelligent Light are working hard to improve the efficiency of wind energy. One aspect they're currently investigating is the use of computational fluid dynamics (CFD) to simulate, predict, and improve the performance of wind farms.
CFD uses supercomputers to model the behavior of fluids such as air or water over moving surfaces. In this instance, UW professor of mechanical engineering Dimitri Mavriplis and Earl Duque at Intelligent Light turned to CFD to study how wind currents transform after coming into contact with a moving turbine's blades in the context of a complete wind farm.
Using the Cheyenne supercomputer at the National Center for Atmospheric Research (NCAR)-Wyoming Supercomputer Alliance, Duque and UW researchers Mavriplis, Michael Brazell and Andrew Kirby were able to model wind farms in unique ways.
What really makes their research unique is the use of in-situ processing. Unlike traditional visualizations produced from stored data, in-situ processing means that visualizations and analysis can be created as a simulation takes place, without writing to disk.
In-situ processing eliminates file transfer bottlenecks, allows increased fidelity, and faster turnaround.
"In-situ has helped reduce the amount of 3D data that we need to store. Just the restart files take 10 TB on a 12-hour run. Automation has gone way up; we have scripts that go straight from simulation to animation. Also, in-situ has helped with debugging—I don't need to pull down 3D data, and I can just use slices, save those to a FieldView XDB file, and then view that on my local machine."Steve Legensky, General Manager and Founder, Intelligent Light
Michael Brazell, University of Wyoming
Intelligent Light: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research under Award #DE-SC0012449.
University of Wyoming:
2016-2017 Blue Waters Fellowship
NSF awards OCI-0725070 and ACI-1238993
NCAR ASD Project
NSF Blue Waters
Office of Naval Research:
ONR Grant N00014-14-1-0045
ONR Grant N00014-16-1-2737
U.S. Department of Energy, Office of Science, Basic Energy Sciences