In recent years, the fluid dynamics community at ASU has been developing robust and efficient numerical codes for fluid dynamics simulations that span from basic fundamental problems concerned with instabilities and transitions to turbulence, and the control of such transitions, in geometrically simple geometries, to large scale oceanographic and atmospheric flow simulations at the micro, meso and global scales and developing strategies to intermesh the different levels. ASU has made a serious commitment to high performance computing with the establishment of the High Performance Computing Initiative (HPCI). While our compute-server needs are presently being met by the ASU HPCI facility, a serious bottleneck to our taking full advantage of this resource is our lack of facilities to interactively visualize and analyze the large data sets that result from our turbulent flow simulations. This is to be addressed with the acquisition of an interactive visualization cluster. The integration of interactive graphical interrogation and analysis of large complex spatio-temporal data sets into the high-performance computing paradigm will lead to a transformation in the way turbulent flows are studied. The visualization cluster will allow us, in a very accessible fashion, to augment the traditional statistical mechanics analyses of the flow fields, which rely on measures involving spatial and temporal averages, with direct analysis of how turbulent coherent structures evolve and interact. The analysis of such 4D data sets (3D spatial plus time) of not only velocity and temperature fields, but also vorticity and other relevant flow quantities such as helicity, is presently only available from large-scale direct numerical simulations. The visual interrogation and analysis of 4D simulation data will play a critical role in transforming our understanding of complex spatio-temporal flows.

The interdisciplinary training of graduate students by mentors who have decades of interdisciplinary research experience between them is a core objective of this proposal. Our REU involvement will be continued by the CSUMS (Computational Science Training for Undergraduates in the Mathematical Sciences) program at ASU, where aspects of the various individual projects described in this proposal will be isolated into projects suitable for undergraduates. For the broader community, our proposed visualization cluster and how we will be utilizing it will provide a paradigm model for interactive graphical visualization and analysis of large spatio-temporal data sets.

National Science Foundation (NSF)
Division of Mathematical Sciences (DMS)
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Dean M Evasius
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Arizona State University
United States
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