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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™.

Knock is one of the main limitations on Spark-Ignited (SI) Internal Combustion Engine (ICE) performance and efficiency and so has been the object of study for over one hundred years. Great strides have been made in terms of understanding in that time, but certain rather elementary practical problems remain. One of these is how to interpret if a running engine is knocking and how likely this is to result in damage. Knocking in a development environment is typically quantified based on numerical descriptions of the high frequency content of a cylinder pressure signal. Certain key frequencies are observed, which Draper [1] explained with fundamental acoustic theory back in 1935. Since then, a number of approaches of varying complexity have been employed to correlate what is happening within the chamber with what is measured by a pressure transducer.

The recent development pace of the automotive technology is so rapid worldwide. Especially in a green car, hybrid electric vehicles (HEVs) have been studied a lot due to their significant effects on the urban driving. In the vehicle energy management strategy study, the driving speed is assumed to be known in advance, however the speed is not given in a real world. Accordingly, the prediction of vehicle speed is very important. In this study, we study the prediction methodology for the speed prediction using deep learning. Based on the vehicle driving speed data, the supervised deep learning has been used and the speed prediction accuracy using deep learning shows accurate results comparing to the actual speed. The supervised deep learning is used which is suitable for driving cycle database. As a result, the speed prediction after few seconds is feasible.

Since the amount of emitted CO2 is directly related to car fuel economy, attention is being drawn to DISI (Direct-Injection Spark-Ignition) engines, which have better fuel economy than conventional gasoline engines. However, it has been a problem that the rich air-fuel mixtures associated with fuel films during cold starts due to spray impingement produce particulate matter (PM). In predicting soot formation, it is important to predict the mixture field precisely. Thus, accurate spray and film models are a prerequisite of the soot model. The previous models were well matched with low-speed collision conditions, such as those of diesel engines, which have a relatively high ambient pressure and long traveling distances. Droplets colliding at low velocities have an order of magnitude of kinetic energy similar to that of the sum of the surface tension energy and the critical energy at which the splash occurs.

The paper reports an activity aiming at characterizing cycle-to-cycle variability (CCV) of the spark-ignition (SI) process in a high performance engine. The numerical simulation of spark-ignition and of early flame kernel evolution are major challenges, mainly due to the time scales of the spark discharge process and to the reduced spatial scales of flame kernel. Typical mesh resolutions are insufficient to resolve the process and a dedicated treatment has to be provided at a subgrid level if the ignition process is to be properly modelled. The focus of this work is on the recent ISSIM-LES (Imposed Stretch Spark-Ignition Model) ignition model, which is based on an extension of the flame surface density (FSD) transport equation for a dedicated flame kernel treatment at subgrid scales. The FSD equation is solved immediately after spark discharge.

It is well known that 3D CFD simulations can give detailed information about fluid and flow properties in complex 3D domains while 1D CFD simulation can provide important information at a system level, i.e. about the performance of the entire engine. The drawbacks of the two simulation methods are that the former requires high computational cost while the latter is not able to capture complex local 3D features of the flow. Therefore, the two simulation methods are to be seen as complementary, indeed a coupling of the two approaches can benefit from the pros of the two methods while minimizing the cons. In particular, with a multi-scale modeling approach (1D-3D) it is possible to simulate large and complex domains by modeling the complex part with a 3D approach and the rest of the domain with a 1D approach.

CFD and FE tools are intensively adopted by engine manufacturers in order to prevent thermo-mechanical failures reducing time- and cost-to market. The capability to predict correctly the physical factors leading to damages is hence essential for their application in the industrial practice. This is even more important for last generation SI engines, where the more and more stringent need to lower fuel consumption and pollutant emissions is pushing designers to reduce engine displacement in favor of higher specific power, usually obtained by means of turbocharging. This brings to a new generation of SI engines characterized by higher and higher adiabatic efficiency and thermo-mechanical loads. A recent research highlighted the different behavior of the thermal boundary layer of such engines operated at high revving speeds and high loads if compared to the same engines operated at low loads and revving speeds or even engines with a lower specific power.

The increasing limitations in engine emissions and fuel consumption have led researchers to the need to accurately predict combustion and related events in gasoline engines. In particular, knock is one of the most limiting factors for modern SI units, severely hindering thermal efficiency improvements. Modern CFD simulations are becoming an affordable instrument to support experimental practice from the early design to the detailed calibration stage. To this aim, combustion and knock models in RANS formalism provide good time-to-solution trade-off allowing to simulate mean flame front propagation and flame brush geometry, as well as “ensemble average” knock tendency in end-gases. Still, the level of confidence in the use of CFD tools strongly relies on the possibility to validate models and methodologies against experimental measurements.

Engine knock is one of the most limiting factors for modern Spark-Ignition (SI) engines to achieve high efficiency targets. The stochastic nature of knock in SI units hinders the predictive capability of RANS knock models, which are based on ensemble averaged quantities. To this aim, a knock model grounded in statistics was recently developed in the RANS formalism. The model is able to infer a presumed log-normal distribution of knocking cycles from a single RANS simulation by means of transport equations for variances and turbulence-derived probability density functions (PDFs) for physical quantities. As a main advantage, the model is able to estimate the earliest knock severity experienced when moving the operating condition into the knocking regime.

It is widely recognized that spatial and temporal evolution of both macro- and micro- turbulent scales inside internal combustion engines affect air-fuel mixing, combustion and pollutants formation. Particularly, in spark ignition engines, tumbling macro-structure induces the generation of a proper turbulence level to sustain the development and propagation of the flame front. As known, 3D-CFD codes are able to describe the evolution of the in-cylinder flow and turbulence fields with good accuracy, although a high computational effort is required. For this reason, only a limited set of operating conditions is usually investigated. On the other hand, thanks to a lower computational burden, 1D codes can be employed to study engine performance in the whole operating domain, despite of a less detailed description of in-cylinder processes. The integration of 1D and 3D approaches appears hence a promising path to combine the advantages of both.

Engine knock is emerging as the main limiting factor for modern spark-ignition (SI) engines, facing increasing thermal loads and seeking demanding efficiency targets. To fulfill these requirements, the engine operating point must be moved as close as possible to the onset of abnormal combustion events. The turbulent regime characterizing in-cylinder flows and SI combustion leads to serious fluctuations between consecutive engine cycles. This forces the engine designer to further distance the target condition from its theoretical optimum, in order to prevent abnormal combustion to severely damage the engine components just because of few individual heavy-knocking cycles. A RANS-based model is presented in this study, which is able to predict not only the ensemble average knock occurrence but also a knock probability. This improves the knock tendency characterization, since the mean knock onset alone is a poorly meaningful indication in a stochastic event such as engine knock.

New SI engine generations are characterized by a simultaneous reduction of the engine displacement and an increase of the brake power; such targets are achieved through the adoption of several techniques such as turbocharging, direct fuel injection, variable valve timing and variable port lengths. This design approach, called “downsizing”, leads to a marked increase in the thermal loads acting on the engine components, in particular on those facing the combustion chamber. Hence, an accurate evaluation of the thermal field is of primary importance in order to avoid mechanical failures. Moreover, the correct evaluation of the temperature distribution improves the prediction of pointwise abnormal combustion onset.

The occurrence of knock is the most limiting hindrance for modern Spark-Ignition (SI) engines. In order to understand its origin and move the operating condition as close as possible to onset of this potentially harmful phenomenon, a joint experimental and numerical investigation is the most recommended approach. A preliminary experimental activity was carried out at IM-CNR on a 0.4 liter GDI unit, equipped with a flat transparent piston. The analysis of flame front morphology allowed to correlate high levels of flame front wrinkling and negative curvature to knock prone operating conditions, such as increased spark timings or high levels of exhaust back-pressure. In this study a detailed CFD analysis is carried out for the same engine and operating point as the experiments. The aim of this activity is to deeper investigate the reasons behind the main outcomes of the experimental campaign.

With the aim of identifying technical solutions to lower the particulate matter emissions, the engine research community made a consistent effort to investigate the root causes leading to soot formation. Nowadays, the computational power increase allows the use of advanced soot emissions models in 3D-CFD turbulent reacting flows simulations. However, the adaptation of soot models originally developed for Diesel applications to gasoline direct injection engines is still an ongoing process. A limited number of studies in literature attempted to model soot produced by gasoline direct injection engines, obtaining a qualitative agreement with the experiments. To the authors’ best knowledge, none of the previous studies provided a methodology to quantitatively match particulate matter, particulate number and particle size distribution function measured at the exhaust without a case-by-case soot model tuning.

The measurement of spray penetration length is one of crucial tasks for understanding the characteristics of diesel spray and combustion. For this reason, many researchers have devised various measurement techniques, including Mie scattering, schlieren photography, and laser induced exciplex fluorescence (LIEF). However, the requirements of expensive lasers, complicated optics, delicate setups, and tracers that affect fuel characteristics have been disadvantages of previous techniques. In this study, the background-oriented schlieren (BOS) technique is employed to measure the vapor penetration length of diesel spray for the first time. The BOS technique has a number of benefits over the previous techniques because of its quantitative, non-intrusive nature which does not require lasers, mirrors, optical filters, or fuel tracers.

The detailed study of part-load conditions is essential to characterize engine-out emissions in key operating conditions. The relevance of part-load operations is further emphasized by the recent regulations such as the new WLTP standard. Combustion development at part-load operations depends on a complex interplay between moderate turbulence levels (low engine speed and tumble ratio), low in-cylinder pressure and temperature, and stoichiometric-to-lean mixture quality (to maximize fuel efficiency). From a modelling standpoint, the reduced turbulence intensity compared to full-load operations complicates the interaction between different sub-models (e.g., reconsideration of the flamelet hypothesis adopted by common combustion models). In this article, the authors focus on chemistry-based simulations for laminar flame speed of gasoline surrogates at conditions typical of part-load operations. The analysis is an extension of a previous study focused on full-load operations.

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.

The paper reports the application of a look-up table approach within a LES combustion modelling framework for the prediction of knock limit in a highly downsized turbocharged DISI engine. During experimental investigations at the engine test bed, high cycle-to-cycle variability was detected even for relatively stable peak power / full load operations of the engine, where knock onset severely limited the overall engine performance. In order to overcome the excessive computational cost of a direct chemical solution within a LES framework, the use of look-up tables for auto-ignition modelling perfectly fits with the strict mesh requirements of a LES simulation, with an acceptable approximation of the actual chemical kinetics. The model here presented is a totally stand-alone tool for autoignition analysis integrated with look-up table reading from detailed chemical kinetic schemes for gasoline.

Ignition delay of the second injection of HSDI diesel engines is generally much shorter than that of the first injection because of the interaction between the radicals generated during the combustion process and the mixed gas of the second injection. Although previous Diesel combustion models could not explain this reaction, Hasse and Peters described the mass and heat transfer of the second injection and estimated the ignition delay of the second injection using two-dimensional flamelet equations. But a simulation of the two-dimensional flamelet equations requires enormous computational time. Thus, to analyze the combustion phenomena of the multiple injection mode in HSDI diesel engines effectively, the two-dimensional flamelet combustion model was modified in this study. To reduce the calculation time, two-dimensional flamelet equations were only applied near the stoichiometric region.

Modeling of unburned hydrocarbon oxidation in an SI engine was performed in engine condition using modified one-step oxidation model. The new one-step equation was developed by modifying the Arrhenius reaction rate coefficients of the conventional one-step model. The modified model was well matched with the results of detailed chemical reaction mechanism in terms of 90 % oxidation time of the fuel. In this modification, the effect of pressure and intermediate species in the burnt gas on the oxidation rate investigated and included in developed one-step model. The effect of pressure was also investigated and included as an additional multiplying factor in the reaction equation. To simulate the oxidation process of piston crevice hydrocarbons, a computational mesh was constructed with fine mesh density at the piston crevice region and the number of cell layers in cylinder was controlled according to the motion of piston.