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A new post-processing technique for analyzing high-dimensional combustion data
KTH, School of Engineering Sciences (SCI), Mechanics. KTH, School of Engineering Sciences (SCI), Centres, Linné Flow Center, FLOW.
KTH, School of Engineering Sciences (SCI), Centres, Linné Flow Center, FLOW. KTH, School of Engineering Sciences (SCI), Mechanics.
2018 (English)In: Combustion and Flame, ISSN 0010-2180, E-ISSN 1556-2921, Vol. 191, p. 226-238Article in journal (Refereed) Published
Abstract [en]

This paper introduces a novel post-processing technique for analyzing high dimensional combustion data. In this technique, t-Distributed Stochastic Neighbor Embedding (t-SNE) is used to reduce the dimensionality of the combustion data with no prior knowledge while preserving the similarity of the original data. Multidimensional combustion datasets are from premixed and non-premixed laminar flame simulations and measurements of a series of well documented piloted flames with inhomogeneous inlets. The resulting reduced manifold is visualized by scatter plots to reveal the global and local structure of the data (manual labeling). Unsupervised clustering algorithms are then utilized for post-processing the t-SNE manifold in order to develop an automatic labeling process. 

Place, publisher, year, edition, pages
ELSEVIER SCIENCE INC , 2018. Vol. 191, p. 226-238
Keywords [en]
Combustion data analysis, Complex chemistry post-processing, Stochastic embedding, Machine learning
National Category
Mechanical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-227742DOI: 10.1016/j.combustflame.2018.01.014ISI: 000430527600021Scopus ID: 2-s2.0-85041682255OAI: oai:DiVA.org:kth-227742DiVA, id: diva2:1205744
Funder
Swedish Energy Agency
Note

QC 20180515

Available from: 2018-05-15 Created: 2018-05-15 Last updated: 2018-05-15Bibliographically approved

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  • apa
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  • de-DE
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  • Other locale
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Output format
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