Supercomputing Improves Predictions of Fluid Flow in Rock
| Autor: | Katie Elyce Jones, James J. Hack, Michael E. Papka |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Cray XK7
General Computer Science business.industry 010102 general mathematics Computational scientist General Engineering Oak Ridge National Laboratory Supercomputer 01 natural sciences Field (computer science) 010305 fluids & plasmas Data modeling Modeling and simulation 0103 physical sciences Fluid dynamics 0101 mathematics Aerospace engineering business Geology ComputingMethodologies_COMPUTERGRAPHICS |
| Zdroj: | Computing in Science & Engineering. 21:74-76 |
| ISSN: | 1558-366X 1521-9615 |
| DOI: | 10.1109/mcse.2019.2930188 |
| Popis: | Reports on the use of supercomputers in the field of geology. The effort to collect and analyze data for a range of geologic materials is time consuming and leaves researchers with only static images of the dynamic process of fluid flow. From 2014 to 2018, researchers led by computational scientist James McClure of Virginia Tech used the 27-petaflop Cray XK7 Titan supercomputer at the US Department of Energy’s (DOE’s), Oak Ridge National Laboratory (ORNL) to advance the team’s computational code for modeling fluid flow in complex, porous geometries. Guided by synchrotron data, McClure’s team models the complex geometries of rocks, then simulates fluid flow based on fundamental physics principles. By combining modeling and simulation with in situ (or real-time) analysis, the team can predict the properties important to large-scale modeling of reservoirs. |
| Databáze: | OpenAIRE |
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