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In an Internal Combustion Engine, the design of the intake system is a very critical aspect since it affects both the engine power output and noise emissions at the intake side. In particular, downsized VVA engines typically produce higher gas-dynamic noise levels since, due to the intake line de-throttling at part-load, a less effective attenuation of the pressure waves is realized. In this work, the acoustic performance of the intake air filter of a commercial VVA engine is numerically and experimentally analyzed. In particular, a FEM model of the system is realized in order to compute the Transmission Loss (TL) parameter of the base device. The numerical analysis accounts of fluid-structure interaction, which gives the possibility to determine the effect of structure participation on the TL profile. Contemporarily, the experimental tests are performed on an acoustic test bench based on the multi-microphone technique for the evaluation of the acoustic parameters.

In the present work, an Auto Regressive Moving Average (ARMA) model and a Discrete Wavelet Transform (DWT) are applied on vibrational signals, acquired by an accelerometer placed on the cylinder block of a Spark Ignition (SI) engine, for knock detection purposes. To the aim of tuning such procedures, the same analysis has been carried out by using the traditional MAPO (Maximum Amplitude of Pressure Oscillations) index and an Inverse Kinetic Model (IKM), both applied on the in-cylinder pressure signals. Vibrational and in-cylinder pressure signals have been collected on a four cylinder, four stroke engine, for different engine speeds, load conditions and spark advances. The results of the two vibrational based methods are compared and in depth discussed to the aim of highlighting the pros and cons of each methodology.

The easiest way to identify knock conditions during the operation of a SI engine is represented by the knowledge of the in-cylinder pressure. Traditional techniques like MAPO (Maximum Amplitude Pressure Oscillation) based method rely on the frequency domain processing of the pressure data. This technique may present uncertainties due to the correct specification of some model parameters, like the band-pass frequency range and the crank angle window of interest. In this paper two innovative techniques for knock detection, which make use of the in-cylinder pressure, are explained in detail, and the results are compared with those coming from the MAPO method. The first procedure is based on the use of statistical analysis by applying an Auto Regressive (AR) technique, while the second technique makes use of the Discrete Wavelet Transform (DWT). The data useful for the analysis have been acquired on a high compression ratio four cylinder, spark ignition engine.