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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.

The growing concerns on the emission of particles smaller than 23 nm, which are harmful to human health, lead to the necessity of introducing a regulation for these particles not yet included in the current emission standards. Considering that measurements of concentration of sub-23 nm particles are particularly sensitive to the sampling conditions, it is important to identify an effective assessment procedure. Aim of this paper is the characterization of the effect of the sampling conditions on sub-23 nm particles, emitted by PFI (port fuel injection) and DI (direct injection) spark ignition engines fueled with gasoline, ethanol and a mixture of ethanol and gasoline (E30). The experimental activity was carried out on a 250 cm3 displacement four stroke GDI and PFI single cylinder engines. The tests were conducted at 2000 rpm and 4000 rpm full load, representative of the homologation urban driving cycle.