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Nowadays the strong knocking combustion involving destructive pressure wave or shock wave has become the main bottleneck for highly boosted engines when pursuing high thermal efficiency. However, its fundamental mechanism is still not fully understood. In this study, synchronization measurements through simultaneous pressure acquisition and high-speed direct photography were performed to comparatively investigate the strong knocking combustion of iso-octane and propane in a rapid compression machine with flat piston design. The pressure characteristics and visualized images of autoignition and reaction wave propagation were compared, and the correlations between thermodynamic trajectories and mixture reactivity progress were analyzed. The results show that iso-octane behaves a greater propensity to strong knocking combustion than propane at similar target pressures.

Noise source identification and separation of internal combustion engines is an effective tool for engine NVH (noise, vibration and harshness) development. Among various experimental approaches, noise source identification using signal processing has attracted extensive attention because of that the signal can be easily acquired and the requirements for equipment is relatively low. In this paper, variational mode decomposition (VMD) combined with independent component analysis (ICA) is used for noise source identification of a turbo-charged gasoline engine. Existing algorithms have been proved to be effective to extract signal features but also have disadvantages. One of the key problems in presently used method is that the main components of the signal, i.e. the main source of the noise, are unknown in advance. Thus the parameters selection of signal processing algorithms, which has a significance influence on the identification result, has no uniform criterion.

Knock threshold detection is the key of closed loop control of ignition in gasoline engine, and it is also the difficult point in knock measurement. In this paper, an investigation of knock detection in turbocharged gasoline engine using bispectrum slice and ensemble empirical mode decomposition (EEMD) based on the engine cylinder head vibration signals is presented. By adding some finite amplitude Gaussian white noises to the signal, EEMD keeps the signal continuous in different time span, and therefore the mode mixing inhering in the classical empirical mode decomposition (EMD) method is alleviated. Power spectrum density (PSD) estimation is used to determine the band range of the resonance frequency generated by knock component. EEMD is used to decompose the original signals, the time-frequency characteristics of the Intrinsic Mode Functions (IMF) are analyzed using Continues Wavelet Transform (CWT) due to its excellent time-frequency resolution.