A new method based on Principal Component Analysis (PCA) was proposed to estimate the dimensionality of the thermo chemical state of a reacting system and to guide the selection of optimal reaction variables. Two experimental data sets, corresponding to a CO/H2/N2 turbulent jet flame and a piloted CH4/air flame, were examined. For both flames, PCA achieved a significant size reduction, although a larger number of optimal variables were required for the CH4/air flame. Non linear deviations in the reconstructed data were observed, suggesting the need of PCA based models able to partition the data sets into local regions characterized by linear dependencies among the variables. This is an abstract of a paper presented at the 2007 AIChE Annual Meeting (Salt Lake City, UT 11/4-9/2007).
Identification of lower dimensional manifolds for turbulent reacting systems via principal component analysis
TOGNOTTI, LEONARDO;
2007-01-01
Abstract
A new method based on Principal Component Analysis (PCA) was proposed to estimate the dimensionality of the thermo chemical state of a reacting system and to guide the selection of optimal reaction variables. Two experimental data sets, corresponding to a CO/H2/N2 turbulent jet flame and a piloted CH4/air flame, were examined. For both flames, PCA achieved a significant size reduction, although a larger number of optimal variables were required for the CH4/air flame. Non linear deviations in the reconstructed data were observed, suggesting the need of PCA based models able to partition the data sets into local regions characterized by linear dependencies among the variables. This is an abstract of a paper presented at the 2007 AIChE Annual Meeting (Salt Lake City, UT 11/4-9/2007).I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
