Capturing Cyclic Variability in SI Engine with Group Independent Component Analysis

Paper #:
  • 2015-24-2415

Published:
  • 2015-09-06
Citation:
Bizon, K., Lombardi, S., Continillo, G., Sementa, P. et al., "Capturing Cyclic Variability in SI Engine with Group Independent Component Analysis," SAE Int. J. Engines 8(5):2042-2049, 2015, https://doi.org/10.4271/2015-24-2415.
Pages:
8
Abstract:
Data decomposition techniques have become a standard approach for the analysis of 2D imaging data originating from optically accessible internal combustion engines. In particular, the method of Proper Orthogonal Decomposition (POD) has proven to be a valuable tool for the evaluation of cycle-to-cycle variability based on luminous combustion imaging and particle image velocimetry (PIV) measurements. POD basically permits to characterize the dominant structures of the process under consideration. Recently, an alternative procedure based on Independent Component Analysis (ICA) has been introduced in the engine field. Unlike POD, the method of ICA identifies the patterns corresponding to physical processes that are statistically independent. In this work, a Group-ICA approach is applied to 2D cycle-resolved images of the luminosity emitted by the combustion process. The analysis is meant to characterize cyclic variability of a port fuel injection spark ignition (PFI SI) engine. For example, any flame front ignited independently by a hot spot is expected to behave independently from the other observed flames. In the Group-ICA approach, image sequences collected synchronically over a number of cycles are grouped together and then analyzed to identify common independent components. These should correspond to the independent phenomena underlying the combustion process at the group level. By this way, a projection is implicitly defined that permits the reconstruction of each member of the group (cycle) through the independent components and their coefficients. The successive analysis of the associated time courses (coefficients), specific for each cycle, permits to capture and discuss differences among cycles.
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