Among plasma-assisted ignition technologies, the Radio-Frequency (RF) corona family represents an interesting solution for the ability to extend the engine operating range. These systems generate transient, non-thermal plasma, which is able to enhance the combustion onset by means of thermal, kinetic and transport effects. Streamer-type RF corona discharge, at about 1 MHz, ignites the air-fuel mixture in multiple filaments, resulting in many different flame kernels. The main issue of this system is that at high electrode voltage and low combustion chamber pressure a transition between streamer and arc easily occurs: in this case transient plasma benefits are lost. A barrier discharge igniter (BDI), supplied with the same RF energy input, instead, is more breakdown-resistant, so that voltage can be raised to higher levels. In this work, a streamer-type RF corona igniter and a BDI were tested in a single-cylinder optical engine fueled with gasoline. Combustion behavior was characterized at stable, near-stable and unstable conditions (depending on IMEP coefficient of variation), increasing the air-fuel ratio starting from a near-stoichiometric mixture. For each test point the maximum allowable electrode voltage for both the igniters was used: in the streamer-type corona case it had to be limited because of the arc transition. Both indicating and imaging analysis were carried out, the latter by means of a high-speed camera that records the natural luminosity of early flames. Raw exhaust gas analysis was carried out as well. Results in terms of cycle-to-cycle variability, combustion duration, pollutant emissions, flame radii evolution and flame growth speed, as well as flame probability presence, are presented. The outcomes of this research can improve the knowledge on streamer-type corona and BDI and can be used to assess the operating range for the control parameters of these igniters.

In order to reduce engine emissions and fuel consumption, extensive research efforts are being devoted to develop innovative ignition devices, able to extend the stable engine operating range towards increasing lean conditions. Among these, radio frequency corona ignition systems, which produce a strong electric field at a frequency of about 1 MHz, can create discharges characterized by simultaneous thermal and kinetic effects. These devices can considerably increase the early flame growth speed, initiating the combustion process in a wide region, as opposed to the local ignition generated by traditional sparks. To explore the corona ignition behavior, experimental campaigns were carried out to investigate different operating conditions, in a constant volume calorimeter designed to measure the deposited thermal energy. The present work compares the combustion development generated by a traditional spark and the corona igniter through computational fluid dynamics simulations. First, simulations are carried out to reproduce the experimental results in the calorimeter, comparing the measured and predicted pressure traces in an inert environment. The validated approach is then applied in a second step to the engine simulations to predict the combustion behavior, using a RANS turbulence model. Computational results are able to reproduce the faster burn rate generated by the corona system in the initial stage of the combustion.

In the wake of previous works, the authors propose a new approach for the identification and evolution of the flame front in an optical SI engine. Currently, it is an essential prerogative to characterize the capability of innovative igniters to guarantee earlier flame development in critical operating conditions, such as ultra-lean mixture, towards which automotive research is moving to deal with the ever more stringent regulations on pollutant emissions. The core of the new approach lies in the R-CNN Mask method. The latter consists of a conceptually simple and general framework for object instance segmentation. It can efficiently detect objects contained in an image while simultaneously generating a high-quality segmentation mask for each instance. In particular, the aim this work is to develop an automatized algorithm for detecting, as objectively as possible, the flame front evolution of lean/ultra-lean mixtures ignited by low-temperature plasma-based ignition systems. The capability of the Mask R-CNN algorithm to automatically estimate the binarized area, without setting a defined binarized threshold, allows us to perform an analysis of the flame front evolution completely independent from the user interpretation. Mask R-CNN can detect the kernel in advance and can identify events as regular combustions instead of misfires or anomalies if compared to other traditional approaches. These features make the proposed method the most suitable option to analysis the real behavior of the innovative ignition systems at critical operating conditions.

Artificial Intelligence is becoming very important and useful in several scientific fields. Machine learning methods, such as neural networks and decision trees, are often proposed in applications for internal combustion engines as virtual sensors, faults diagnosis systems and engine performance optimization. The high pressure of the intake air coupled with the demand of lean conditions, in order to reduce emissions, have often close relationship with the knock events. Fuels autoignition characteristics and flame front speed have a significant impact on knock phenomenon, producing high internal cylinder pressures and engine faults.The limitations in using pressure sensors in the racing field and the challenge to reduce the costs of commercial cars, push the replacement of a hardware redundancy with a software redundancy. Therefore, it becomes strategically important to develop a robust predictive model that, using the physical properties such as air temperature and pressure, fuel consumption and engine speed, could increase the engine performance under a large range of operating conditions, without computational efforts.In this paper, three machine learning approaches were implemented to predict the knock onset and knock intensity of a SI engine. The tool is fed by several input variables coming from a CFD-1D engine model whose calibration has been performed by using experimental data. Input parameters influencing the knock phenomenon, such as engine speed, air-fuel ratio, max internal cylinder pressure, combustion timing, and physical air conditions in the plenum, have been used as dataset for training and test phases. Once trained, the machine learning models were tested on their ability to predict outputs based on samples not used during the training set. The outputs predicted were compared with the target ones and the accuracy of the model was evaluated in terms of RMS and R2.

In recent years, radio-frequency corona ignition gained increasing interest from the engine research community because of its capability to extend the engine stable operating range in terms of lean and EGR dilution. The corona discharge generates streamers coming from a star-shaped electrode, generally consisting of four or five tips. The temporal and spatial variability of such streamers in length, orientation, and branching can be factors that affect the combustion onset and, therefore, engine cycle-to-cycle variability. Generally, the latter is reduced with respect to a conventional spark igniter at the same air–fuel ratio, but still present. In this work, analysis on the corona discharge and on the subsequent combustion onset was carried out in an optically accessible engine by means of the detection, via high-speed camera, of the natural luminosity of streamers and flames. A method to characterize spatial and temporal variability in motored conditions is firstly presented. A statistical analysis of the streamer behavior was performed, by separately analyzing the streamers generated by each tip of the star-shaped electrode. Finally, an original method aimed at determining the moment of the first flame appearance, caused by the combustion onset, is presented. The outcome of this work can be used to improve the knowledge on corona discharge, in particular on the stochastic behavior that characterizes the streamers. The presented optical analysis can also be adapted to other volumetric, single- or multi-point ignition systems.