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Dimensionality Reduction Using a Dominant Dynamic Variable, Self Similarity and Data Tabulation: Application to Hydrocarbon Oxidation

Kourdis, Panagiotis D. and Bellan, Josette and Harstad, Kenneth (2014) Dimensionality Reduction Using a Dominant Dynamic Variable, Self Similarity and Data Tabulation: Application to Hydrocarbon Oxidation. In: 52nd Aerospace Sciences Meeting. American Institute of Aeronautics and Astronautics , Reston, VA, Art. No. 2014-0819. ISBN 978-1-62410-256-1. https://resolver.caltech.edu/CaltechAUTHORS:20190819-150530040

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Abstract

A dimensionality reduction method is developed for autonomous dynamical systems exploiting the local (near) self similarity due to the presence of a dominant dynamic variable. The method is coupled with a simple tabulation scheme to take advantage of the computationally more efficient local partial self similarity. The proposed methodology is used to construct reduced kinetics models of hydrocarbon oxidation and is tested for a n-dodecane/air mixture.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.2514/6.2014-0819DOIArticle
ORCID:
AuthorORCID
Bellan, Josette0000-0001-9218-7017
Additional Information:© 2014 American Institute of Aeronautics and Astronautics. Published Online: 10 Jan 2014.
Other Numbering System:
Other Numbering System NameOther Numbering System ID
AIAA Paper2014-0819
DOI:10.2514/6.2014-0819
Record Number:CaltechAUTHORS:20190819-150530040
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20190819-150530040
Official Citation:Dimensionality Reduction Using a Dominant Dynamic Variable, Self Similarity and Data Tabulation: Application to Hydrocarbon Oxidation. Panagiotis Kourdis, Josette R. Bellan, and Kenneth Harstad. 52nd Aerospace Sciences Meeting. January 2014. doi: 10.2514/6.2014-0819
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:98009
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:19 Aug 2019 22:28
Last Modified:16 Nov 2021 17:35

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