Abstract
The study of fluid flow turbulence has been an active area of research for over 100 years, mainly because of its technological importance to a vast number of applications. In recent times with the advent of supercomputers and new experimental imaging techniques, terabyte scale data sets are being generated, and hence storage as well as analysis of this data has become a major issue. In this chapter we outline a new approach to tackling these data-sets which relies on selective data storage based on real-time feature extraction and utilizing data mining tools to aid in the discovery and analysis of the data. Visualization results are presented which highlight the type and number of spatially and temporally evolving coherent features that can be extracted from the data sets as well as other high level features.
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Marusic, I., Candler, G.V., Interrante, V., Subbareddy, P.K., Moss, A. (2001). Real Time Feature Extraction for the Analysis of Turbulent Flows. In: Grossman, R.L., Kamath, C., Kegelmeyer, P., Kumar, V., Namburu, R.R. (eds) Data Mining for Scientific and Engineering Applications. Massive Computing, vol 2. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-1733-7_13
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DOI: https://doi.org/10.1007/978-1-4615-1733-7_13
Publisher Name: Springer, Boston, MA
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