Aerospace and Mechanical Engineering
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image of turbulance flow

National Science Foundation: Dynamic Feature Extraction & Data Mining for the Analysis of Turbulent Flows

Principal Investigator: Professor Ivan Marusic

Co-Investigators:

Professor Graham Candler Aerospace Engineering and Mechanics
Professor Ellen Longmire Aerospace Engineering and Mechanics
Professor Sean Garrick Mechanical Engineering
Professor Vipin Kumar Computer Science
Professor Victoria Interrante Computer Science
Professor George Karypis Computer Science

Project Summary

This research program unites experts from data visualization and data mining with those from experimental and computational fluid dynamics to develop new computational tools for the quantitative visualization and analysis of large data sets. The need for this work has become apparent with the recent rapid advances in computational and experimental capabilities, which have advanced to the point that they have out-stripped our ability to analyze the data.

The objective of the proposed research is to develop a new class of data analysis methods. This will involve on-the-fly identification of physically-important events to selectively store the data. This database will then be interrogated by feature extraction algorithms to yield an additional object database, which is a compact representation of the physically important space-time object trajectories. These data objects will be used as input to novel data mining methods to discover causal relationships between the objects. This approach will result in efficient storage of data, visualization of the important events within the data set, and methods for high-level analysis of relationships between objects in the data.

The test bed problems for the development of these methods are turbulent boundary layer flows and turbulent combustion. These databases have been chosen because of their technological importance and because they offer different challenges to the method. We will consider two specific events in these flows: drag-producing bursts in boundary layers, and soot formation in combustion. This integrated approach of feature identification, selective storage of data, and advanced analysis will hopefully result in a better physical understanding of fluid turbulence. This would then potentially result in the development of mechanisms for drag reduction and reduced soot emissions. More generally, the approach may be applied to a wide range of scientific simulations and experiments, and represents a new way to analyze large data sets.


Last Modified: 2011-07-01 at 10:45:18 -- this is in International Standard Date and Time Notation