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Diffusion-Weighted Magnetic Resonance Signal for General Gradient Waveforms: Multiple Correlation Function Framework, Path Integrals, and Parallels Between Them Read chapter Read first chapter

2015 | OriginalPaper | Chapter

A Clustering Method for Identifying Regions of Interest in Turbulent Combustion Tensor Fields

Authors : Adrian Maries, Timothy Luciani, P. H. Pisciuneri, Mehdi B. Nik, S. Levent Yilmaz, Peyman Givi, G. Elisabeta Marai

Published in: Visualization and Processing of Higher Order Descriptors for Multi-Valued Data

Publisher: Springer International Publishing

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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.

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previous chapter Statistical and Machine Learning Methods for Neuroimaging: Examples, Challenges, and Extensions to Diffusion Imaging Data
next chapter Tensor Lines in Engineering: Success, Failure, and Open Questions
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Metadata
Title
A Clustering Method for Identifying Regions of Interest in Turbulent Combustion Tensor Fields
Authors
Adrian Maries
Timothy Luciani
P. H. Pisciuneri
Mehdi B. Nik
S. Levent Yilmaz
Peyman Givi
G. Elisabeta Marai
Copyright Year
2015
Book
Visualization and Processing of Higher Order Descriptors for Multi-Valued Data

Print ISBN: 978-3-319-15089-5

Electronic ISBN: 978-3-319-15090-1

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-15090-1_16

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