A Clustering Method for Identifying Regions of Interest in Turbulent Combustion Tensor Fields
Conference paper
Abstract
Production of electricity and propulsion systems involve turbulent combustion. Computational modeling of turbulent combustion can improve the efficiency of these processes. However, large tensor datasets are the result of such simulations; these datasets are difficult to visualize and analyze. In this work we present an unsupervised statistical approach for the segmentation, visualization and potentially the tracking of regions of interest in large tensor data. The approach employs a machine learning clustering algorithm to locate and identify areas of interest based on specified parameters such as strain tensor value. Evaluation on two combustion datasets shows this approach can assist in the visual analysis of the combustion tensor field.
Keywords
Mach Number Large Eddy Simulation Direct Numerical Simulation Tensor Field Turbulent Combustion
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Notes
Acknowledgements
This work was supported by NSF CBET-1250171 and NSF CAREER IIS-0952720.
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