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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.

Flame luminosity fields can nowadays be collected from optically accessible engines, with high spatial and temporal resolution, and constitute a very powerful investigation means for the transient combustion phenomena taking place in the engine chamber. Interpretation of the impressive amount of collected data can be quite challenging, mainly due to the variety of coupled phenomena involved. Application of Independent Component Analysis (ICA) aims here at separating spatial structures related to different combustion events, and is coupled with the analysis of the statistics of the coefficients of the independent components, and of the measured in-cylinder parameters. This paper reports on the comparison of the application of ICA to 2D images of combustion-related luminosity collected from two different optically accessible engines: Diesel and spark ignition.