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In the present paper, two different methodologies are adopted and critically integrated to analyze the knock behavior of a last generation small size spark ignition (SI) turbocharged VVA engine. Particularly, two full load operating points are selected, exhibiting relevant differences in terms of knock proximity. On one side, a knock investigation is carried out by means of an Auto-Regressive technique (AR model) to process experimental in-cylinder pressure signals. This mathematical procedure is used to estimate the statistical distribution of knocking cycles and provide a validation of the following 1D-3D knock investigations. On the other side, an integrated numerical approach is set up, based on the synergic use of 1D and 3D simulation tools. The 1D engine model is developed within the commercial software GT-Power™. It is used to provide time-varying boundary conditions (BCs) for the 3D code, Star-CD™.

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.

In this paper, a high performance V12 spark-ignition engine is experimentally investigated at test-bench in order to fully characterize its behavior in terms of both average parameters, cycle-by-cycle variations and knock tendency, for different operating conditions. In particular, for each considered operating point, a spark advance sweep is actuated, starting from a knock-free calibration, up to intense knock operation. Sequences of 300 consecutive pressure cycles are measured for each cylinder, together with the main overall engine performance, including fuel flow, torque, and fuel consumption. Acquired data are statistically analyzed to derive the distributions of main indicated parameters, in order to find proper correlations with ensemble-averaged quantities. In particular, the Coefficient of Variation (CoV) of IMEP and of the in-cylinder peak pressure (pmax) are correlated to the average combustion phasing and duration (MFB50 and Δθb), with a good coefficient of determination.

In this paper, the results of an extensive experimental analysis regarding a twin-cylinder spark-ignition turbocharged engine are employed to build up an advanced 1D model, which includes the effects of cycle-by-cycle variations (CCVs) on the combustion process. Objective of the activity is to numerically estimate the CCV impact primarily on fuel consumption and knock behavior. To this aim, the engine is experimentally characterized in terms of average performance parameters and CCVs at high and low load operation. In particular, both a spark advance and an air-to-fuel ratio (α) sweep are actuated. Acquired pressure signals are processed to estimate the rate of heat release and the main combustion events. Moreover, the Coefficient of Variation of IMEP (CoVIMEP) and of in-cylinder peak pressure (CoVpmax) are evaluated to quantify the cyclic dispersion and identify its dependency on peak pressure position.

Knock occurrence is a widely recognized phenomenon to be controlled during the development and optimization of S.I. engines, since it bounds both compression ratio and spark advance, hence reducing the potential in gaining a lower fuel consumption. As a consequence, a clear understanding of the engine parameters affecting the onset of auto-ignition is mandatory for the engine setup. In view of the complexity of the phenomena, the use of combined experimental and numerical investigations is very promising. The paper reports such a combined activity, targeted at characterizing the combustion behavior of a small unit displacement two-stroke SI engine operated with either Gasoline or Natural Gas (CNG). In the paper, detailed multi-cycle 3D-CFD analyses, starting for preliminary 1D computed boundary conditions, are performed to accurately characterize the engine behavior in terms of scavenging efficiency and combustion.

In this paper, an experimental and numerical analysis of combustion process and knock occurrence in a small displacement spark-ignition engine is presented. A wide experimental campaign is preliminarily carried out in order to fully characterize the engine behavior in different operating conditions. In particular, the acquisition of a large number of consecutive pressure cycle is realized to analyze the Cyclic Variability (CV) effects in terms of Indicated Mean Effective Pressure (IMEP) Coefficient of Variation (CoV). The spark advance is also changed up to incipient knocking conditions, basing on a proper definition of a knock index. The latter is estimated through the decomposition and the FFT analysis of the instantaneous pressure cycles. Contemporary, a quasi-dimensional combustion and knock model, included within a whole engine one-dimensional (1D) modeling framework, are developed. Combustion and knock models are extended to include the CV effects, too.

During the last years, a number of techniques aimed at the experimental identification of the knocking onset in Spark-Ignition (SI) Internal Combustion Engines have been proposed. Besides the traditional procedures based on the processing of in-cylinder pressure data in the frequency domain, in the present paper two innovative methods are developed and compared. The first one is based on the use of statistical analysis by applying an Auto Regressive Moving Average (ARMA) technique, coupled to a prediction algorithm. It is shown that such parametric model, applied to the instantaneous in-cylinder pressure measurements, is highly sensitive to knock occurrence and is able to identify soft or heavy knock presence in different engine operating conditions. An alternative, more expensive procedure is developed and compared to the previous one.

Control of knock phenomenon is becoming more and more important in modern SI engine, due to the tendency to develop high boosted turbocharged engines (downsizing). To this aim, improved modeling and experimental techniques are required to precisely define the maximum allowable spark advance. On the experimental side, the knock limit is identified based on some indices derived by the analysis of the in-cylinder pressure traces or of the cylinder block vibrations. The threshold levels of the knock indices are usually defined following an heuristic approach. On the modeling side, in the 1D codes, the knock is usually described by simple correlation of the auto-ignition time of the unburned gas zone within the cylinders. In addition, the latter methodology commonly refers to ensemble-averaged pressure cycles and, for this reason, does not take into account the cycle-by-cycle variations.

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.

This work presents the results of an experimental investigation on a GDI injector, in order to analyze fuel injection process and atomization phenomenon, correlating imaging and vibro-acoustic diagnostic techniques. A single-hole, axially-disposed, 0.200 mm diameter GDI injector was used to spray commercial gasoline in a test chamber at room temperature and atmospheric backpressure. The explored injection pressures were ranged from 5.0 to 20.0 MPa. Cycle-resolved acquisitions of the spray evolution were acquired by a high-speed camera. Simultaneously, the vibro-acoustic response of the injector was evaluated. More in detail, noise data acquired by a microphone sensor were analyzed for characterizing the acoustic emission of the injection, while a spherical loudspeaker was used to excite the spray injection at a proper distance detecting possible fuel spray resonance phenomena.