Direct Injection Spark Ignition Engine Research Articles

A Transported Livengood–Wu Integral Model for Knock Prediction in Computational Fluid Dynamics Simulation

Abstract This work describes the development of a transported Livengood–Wu (L–W) integral model for computational fluid dynamics (CFD) simulation to predict autoignition and engine knock tendency. The currently employed L–W integral model considers both single-stage and two-stage ignition processes, thus can be generally applied to different fuels such as paraffin, olefin, aromatics, and alcohol. The model implementation is first validated in simulations of homogeneous charge compression ignition (HCCI) combustion for three different fuels, showing good accuracy in prediction of autoignition timing for fuels with either single-stage or two-stage ignition characteristics. Then, the L–W integral model is coupled with G-equation model to indicate end-gas autoignition and knock tendency in CFD simulations of a direct-injection spark-ignition engine. This modeling approach is about 10 times more efficient than the ones that based on detailed chemistry calculation and pressure oscillation analysis. Two fuels with same Research Octane Number (RON) but different octane sensitivity are studied, namely, Co-Optima alkylate and Co-Optima E30. Feed-forward neural network model in conjunction with multivariable minimization technique is used to generate fuel surrogates with targets of matched RON, octane sensitivity, and ethanol content. The CFD model is validated against experimental data in terms of pressure traces and heat release rate for both fuels under a wide range of operating conditions. The knock tendency—indicated by the fuel energy contained in the autoignited region—of the two fuels at different load conditions correlates well with the experimental results and the fuel octane sensitivity, implying the current knock modeling approach can capture the octane sensitivity effect and can be applied to further investigation on composition of octane sensitivity.

Experimental and numerical investigation of evaporating fuel films in combustion

Fuel films evaporating throughout combustion can cause soot formation in spark-ignited direct-injection (DI) engines. In this work, laser-induced fluorescence (LIF), combustion imaging, 3D computational fluid dynamics (CFD), and a low-dimensional model (LDM) were used to investigate fuel-film evaporation, combustion, and soot formation in an atmospheric-pressure constant-flow facility that serves as a DI model experiment. A six-hole injector laterally injects isooctane doped with toluene as a fluorescent tracer. Most of the fuel evaporates into the preheated air crossflow, but some impinges on a quartz window on the opposite side, forming fuel films. The fuel/air-mixture can be ignited by spark electrodes protruding into the chamber. LIF was used to image the fuel-film thickness at different times after start of injection (aSOI). From that, the film mass and evaporation rate are calculated. The large eddy simulation (LES) CFD employs a Lagrangian treatment for the dispersed phase in combination with models for spray/wall interaction and the wall film. CFD and LIF consistently find that evaporation rates are highest for early times aSOI and remain constant from about 10 ms aSOI. At ambient temperature the evaporation rates from LIF and CFD are almost the same while at elevated temperatures the CFD predicts about a two times higher evaporation rate than measured. LIF, CFD, and LDM reveal a strong dependence of the evaporation rate on the wall temperature, while there is very little influence of combustion and thus convective heat transfer. The simulations show that the fuel-film temperature rapidly reaches the wall temperature that remains approximately constant throughout evaporation. High-speed combustion imaging and CFD show the inception of soot pockets at similar times, close to the evaporating fuel films, and consistent in spatial extent.

Multi-parameter imaging of in-cylinder processes during transient engine operation for the investigation of soot formation

Transient engine operation of direct-injection spark ignition engines can result in high particulate number emissions. To investigate the causes of soot formation, an engine test rig was developed to perform detailed measurements of real transient operation. For this purpose, a single-cylinder full-metal engine with a real combustion chamber geometry was equipped with minimally invasive optical accesses. Simultaneous high-speed endoscopic PIV, spray visualization, and combustion imaging were applied to investigate the in-cylinder processes in detail. Endoscopic PIV was first compared in the central symmetry plane with classical PIV performed at the equivalent optical engine at steady-state operation for verification. Then the engine parameters of a tip-in performed by the corresponding four-cylinder engine, which led to high particle number emissions, were applied to the single-cylinder engine. The engine parameters were in a good agreement and particle number emissions due to the maneuver were within the same range of several 106 #/cm3. In total, 19 repetitions of the tip-in maneuver were analyzed with respect to the in-cylinder processes and repeatability of engine parameters. Furthermore, the in-cylinder flow field during the late compression stroke, flame propagation, and soot luminosity of single cycles during the tip-in indicated cause-and-effect chains for the formation of pool fire and soot at the injector tip. The direction of the flow below the spark plug influenced the direction of flame propagation. An early arrival of the flame enhanced the formation of soot from fuel films formed on the piston surface or at the injector. In engine applications, counter measures can be applied to reduce the particle number emissions when accounting for these indicated cause-and-effect chains.

G-equation based ignition model for direct injection spark ignition engines

To accurately model the Direct Injection Spark Ignition (DISI) combustion process, it is important to account for the effects of the spark energy discharge process. The proximity of the injected fuel spray and spark electrodes leads to steep gradients in local velocities and equivalence ratios, particularly under cold-start conditions when multiple injection strategies are employed. The variations in the local properties at the spark plug location play a significant role in the growth of the initial flame kernel established by the spark and its subsequent evolution into a turbulent flame. In the present work, an ignition model is presented that is compatible with the G-Equation combustion model, which responds to the effects of spark energy discharge and the associated plasma expansion effects. The model is referred to as the Plasma Velocity on G-surface (PVG) model, and it uses the G-surface to capture the early kernel growth. The model derives its theory from the Discrete Particle Ignition (DPIK) model, which accounts for the effects of electrode heat transfer, spark energy, and chemical heat release from the fuel on the early flame kernel growth. The local turbulent flame speed has been calculated based on the instantaneous location of the flame kernel on the Borghi-Peters regime diagram. The model has been validated against the experimental measurements given by Maly and Vogel,1 and the constant volume flame growth measurements provided by Nwagwe et al.2 Multi-cycle simulations were performed in CONVERGE3 using the PVG ignition model in combination with the G-Equation-based GLR4 model in a RANS framework to capture the combustion characteristics of a DISI engine. Good agreements with the experimental pressure trace and apparent heat-release rates were obtained. Additionally, the PVG ignition model was observed to substantially reduce the sensitivity of the default G-sourcing ignition method employed by CONVERGE.

Evolution of methods applied for assessing harmful engine deposits caused by gasoline combustion

The article describes the threat posed by deposits harmful to the proper functioning of spark ignition engines. The areas of indirect and direct injection engines where the most dangerous deposits form are indicated. The factors having significant influence on the occurrence of this unfavourable phenomenon were collected and analyzed. Consequently, a simplified classification of factors influencing the formation of harmful deposits in direct and indirect injection spark ignition engines was made. In the research part of the project, a comparative study of the tendency of gasolines of different composition and physicochemical properties to form deposits was carried out. The criterion for evaluating the detergent properties of gasolines was the tendency to form deposits on intake valves in the case of indirect injection engine and on the injector in the case of direct injection engine. For this purpose, the previously widely used test procedure CEC F-05-93 relating to deposits formed on intake valves in SI indirect injection engines and the latest test procedure CEC F-113-KC relating to the most harmful deposits formed in injectors of DISI (Direct Injection Spark Ignition) engines were used. The purpose of the comparative study conducted was to determine if there was any relatively simple, identifiable relationship between the results of gasoline detergent property evaluations obtained at engine test sites differing in test engine generations, methods of conducting the evaluations, and type of engine deposits formed. As a result, no correlations were found between the testable engine sludge tendency results obtained from tests using the CEC F-05-93 and CEC F-113-KC procedures. Therefore, knowing the evaluation of gasoline conducted according to one of the above mentioned test procedures, one cannot conclude, predict or estimate the evaluation that will be obtained according to the other test procedure. Therefore, the results obtained according to one of the procedures do not allow extrapolation and evaluation of gasoline in terms of tendency to form harmful engine deposits according to the other procedure.

Manifold reduction techniques for the comparison of crank angle-resolved particle image velocimetry (PIV) data and Reynolds-averaged Navier-Stokes (RANS) simulations in a spark ignition direct injection (SIDI) engine

In this article, different manifold reduction techniques are implemented for the post-processing of Particle Image Velocimetry (PIV) images from a Spark Ignition Direct Injection (SIDI) engine. The methods are proposed to help make a more objective comparison between Reynolds-averaged Navier-Stokes (RANS) simulations and PIV experiments when Cycle-to-Cycle Variations (CCV) are present in the flow field. The two different methods used here are based on Singular Value Decomposition (SVD) principles where Proper Orthogonal Decomposition (POD) and Kernel Principal Component Analysis (KPCA) are used for representing linear and non-linear manifold reduction techniques. To the authors’ best knowledge, this is the first time a non-linear manifold reduction technique, such as KPCA, has ever been used in the study of in-cylinder flow fields. Both qualitative and quantitative studies are given to show the capability of each method in validating the simulation and incorporating CCV for each engine cycle. Traditional Relevance Index (RI) and two other previously developed novel indexes: the Weighted Relevance Index (WRI) and the Weighted Magnitude Index (WMI), are used for the quantitative study. The results indicate that both POD and KPCA show improvements in capturing the main flow field features compared to ensemble-averaged PIV experimental data and single cycle experimental flow fields while capturing CCV. Both methods present similar quantitative accuracy when using the three indexes. However, challenges were highlighted in the POD method for the selection of the number of POD modes needed for a representative reconstruction. When the flow field region presents a Gaussian distribution, the KPCA method is seen to provide a more objective numerical process as the reconstructed flow field will see convergence with an increasing number of modes due to its usage of Gaussian properties. No additional criterion is needed to determine how to reconstruct the main flow field feature. Using KPCA can, therefore, reduce the amount of analysis needed in the process of extracting the main flow field while incorporating CCV.

Experimental assessment of ignition characteristics of lubricating oil sprays related to low-speed pre-ignition (LSPI)

Low-speed pre-ignition (LSPI) remains one of the challenges of Direct Injection (DI) Spark Ignition (SI) engines due to its potential to induce a heavy knock. Several mechanisms have been identified in the literature as plausible causes for LSPI. The physical and chemical properties of lubricant oils play a role on some of these causes. The present work aims at getting an independent procedure to determine the proneness of lubricant oils to ignite. To this end, the ignition delay (ID) of different oil formulations is experimentally determined in a constant-pressure flow facility through two different optical techniques: Schlieren and OH* chemiluminescence imaging. The investigation explores the effect of base-stock formulation, oil specification quality level, different additive types content, aging, and oxidation on oil reactivity for several thermodynamic conditions. Differences in ignition delay were found among base stocks, correlating with the American Petroleum Institute (API) group classification. However, no significant differences were found among additive packages previously reported to yield different LSPI occurrences. Hence, differences in reactivity among lubricating oil formulations are not the determining factor explaining their different LSPI occurrences in an engine. Similarly, specific lubricant additive content, aging, and oxidation do not importantly modify the measured ignition delay.

Combustion characteristics and engine performance of 2-methylfuran compared to gasoline and ethanol in a direct injection spark ignition engine

2-Methylfuran (MF) is considered as a promising second-generation biofuel for spark ignition engines. MF has better anti-knock ability, and the lower heating value of MF is close to that of conventional fuels. The production methods of MF have been largely improved over the last decade. However, the studies of MF on direct injection spark ignition (DISI) engines are quite limited, and little is known about the effects of engine control parameters on MF combustion at typical DISI engine operating conditions. In the present work, the effects of engine control parameters including spark timing, air–fuel ratio, injection timing, and injection pressure on combustion and engine performances of MF were studied at typical medium load and high load conditions on a single-cylinder spray-guided DISI engine, compared to gasoline and ethanol. The results show that the achievable spark timing range considering combustion phasing and knock limit for MF is larger than that of gasoline especially at high load. MF has more robustness to lean burn than gasoline and ethanol. The combustion characteristics and heat release of MF have low sensibility to the variation of injection timings. To achieve the same engine load MF requires up to 25% longer injection durations than gasoline, however, increased injection pressure can mitigate the injection duration differences between MF and gasoline.

Droplets velocity and diameter variations of wall impinging spray created by slicer

Microscopic characteristics of the impinging spray have a significant effect on atomization and pollutant emissions. Therefore, understanding the droplet behavior of the impinging spray comprehensively is essential to evaluate the spray atomization characteristics. However, in experiments, the size and distribution of droplets are hardly investigated in direct-injection spark-ignition (DISI) engines because of the high droplet density during the fuel injection period. In this paper, a spray slicer was adopted to cut the dense spray into thin flake with the objective of observing discrete droplets. And the particle image analysis (PIA) technique was used to detect droplet behaviors at different locations under various injection pressures of 10, 20, and 30 MPa. The results showed that the number density and velocity of droplets at the spray tip gradually decreased along the direction of spray development. The droplet away from the wall has a larger Y component of velocity (Vy). Furthermore, with an increase in injection pressure, the droplet velocity at the spray tip increased dramatically, and the atomization was greatly improved. Besides, sauter mean diameter (SMD) significantly declined at the upstream. However, Weber number (We) was almost the same at the fixed downstream locations under all injection pressures. Finally, We distribution of droplets concentrated at significantly smaller values under the fixed pressure. With an increase in injection pressure, We of droplets distributed near a large value range at all fixed locations.

Large Eddy Simulation of Lean Mixed-Mode Combustion Assisted by Partial Fuel Stratification in a Spark-Ignition Engine

Abstract Partial fuel stratification (PFS) is a promising fuel injection strategy to improve the stability of lean combustion by applying a small amount of pilot injection right before spark timing. Mixed-mode combustion, which makes use of end-gas autoignition following conventional deflagration-based combustion, can be further utilized to speed up the overall combustion. In this study, PFS-assisted mixed-mode combustion in a lean-burn direct injection spark-ignition (DISI) engine is numerically investigated using multi-cycle large eddy simulation (LES). A previously developed hybrid G-equation/well-stirred reactor combustion model for the well-mixed operation is extended to the PFS-assisted operation. The experimental spray morphology is employed to derive spray model parameters for the pilot injection. The LES-based model is validated against experimental data and is further compared with the Reynolds-averaged Navier–Stokes (RANS)-based model. Overall, both RANS and LES predict the mean pressure and heat release rate traces well, while LES outperforms RANS in capturing the cycle-to-cycle variation (CCV) and the combustion phasing in the mass burned space. Liquid and vapor penetrations obtained from the simulations agree reasonably well with the experiment. Detailed flame structures predicted from the simulations reveal the transition from a sooting diffusion flame to a lean premixed flame, which is consistent with experimental findings. LES captures more wrinkled and stretched flames than RANS. Finally, the LES model is employed to investigate the impacts of fuel properties, including heat of vaporization (HoV) and laminar burning speed (SL). Combustion phasing is found more sensitive to SL than to HoV, with a larger fuel property sensitivity of the heat release rate from autoignition than that from deflagration. Moreover, the combustion phasing in the PFS-assisted operation is shown to be less sensitive to SL compared with the well-mixed operation.

Combining machine learning with 3D-CFD modeling for optimizing a DISI engine performance during cold-start

This work presents a methodology for using machine learning (ML) techniques in combination with 3D computational fluid dynamics (CFD) modeling to optimize the cold-start fast-idle phase of a gasoline direct injection spark ignition (DISI) engine. The optimization process implies the identification of the range of operating parameters, that will ensure the following criteria under cold-start conditions: (1) a fixed IMEP of 2 bar (BMEP of 0 bar), (2) a stoichiometric exhaust equivalence ratio (based on carbon-to-oxygen atoms) to ensure the efficient operation of the after-treatment system, (3) enough exhaust heat flux to ensure a rapid light-off of the after-treatment system, and (4) reduced NOx and HC emissions. A total of six operating parameters will be identified as having a significant influence on cold-start engine performance. These parameters are associated with the fuel injection strategy (end of the second injection, injection pressure, and fuel mass); combustion strategy (spark timing, spark energy); and intake airflow (intake manifold pressure). Performing an optimization study exclusively using multi-cycle (at least 3 cycles) 3D CFD simulations would be an arduous task. For example, to achieve an exhaust equivalence ratio of 1 and an IMEP of 2 bar, multiple iterations would be required for fuel mass (to account for film formation), intake manifold pressure (to ensure enough air in-cylinder), and spark timing (to ensure the fixed load). This process would be more convoluted and expensive with the addition of constraints for exhaust heat flux and emissions. A promising approach to tackling such a complicated optimization process is to employ the concept of machine learning, which demands a database formed by the six operating parameters mentioned above. The current work will demonstrate a strategy of combining CFD modeling with advanced Gaussian Process Regression (GPR)-based ML models to make predictions about DISI cold-start behavior with acceptable accuracy and a substantially reduced computational time.

Market Fuel Effects on Low Speed Pre-Ignition

<div class="section abstract"><div class="htmlview paragraph">Low-Speed Pre-ignition (LSPI) is an undesirable abnormal combustion phenomenon observed in turbocharged, direct-injection spark-ignition engines and is characterized by early heat release, high cylinder pressures and severe, potentially damaging knock. LSPI has been studied for more than a decade and engine design, operating conditions and fuel and engine oil formulations have all been identified as contributing factors. A significant focus on engine oil has led to the establishment of the Sequence IX engine test and the second-generation of GM dexos® oil requirements, as well as a convergence of engine oil detergent causality. Conclusions about the effects of fuel on LSPI have been more varied, but as part of a recently completed research consortium, the LSPI tendency of market fuels with a range of properties, including composition, boiling point distribution, ethanol content and particulate matter index (PMI) were evaluated. Tests were performed in a 2-liter GM LHU engine and each test comprised of at least 24 repeats of a high-load, low-speed, steady-state test segment, with engine boundary conditions and calibration adjusted to amplify LSPI. All market fuel tests were bracketed by baseline tests and severity adjustments were made to account for changes in engine condition. It was found that the PMI and certain boiling points correlated the best with the frequency of LSPI events. In addition, decreased LSPI severity, quantified by the peak-to-peak knock values, was found to correspond with increased octane numbers and higher ethanol content of the market fuels.</div></div>

On Optical Semi-Quantitative Spectral Study of Low-Speed Pre-Ignition Sources in Spark Ignition Engines

<div class="section abstract"><div class="htmlview paragraph">Low-Speed pre-ignition (LSPI) in modern-day, heavily downsized, boosted, and direct-injection spark ignition (SI) engines is a well-known problem. Several mechanisms contribute towards stochastic pre-ignition (SPI), the most prominent being crevice material droplet induced and deposit induced pre-ignition mechanisms. The droplet mechanism is typically dominated by the detergent additives present in the lubricant formulation; more specifically calcium and sodium-based detergent additives correlate strongly with the increased LSPI rates. Deposits flaking off the combustion chamber surfaces can also induce LSPI under certain conditions.</div><div class="htmlview paragraph">This study aimed to develop an optical method designed to investigate the nature of pre-ignition precursors. Southwest Research Institute (SwRI) utilized an optically accessible GM 2.0 L LHU engine to study the pre-ignition phenomenon and studied the nature of pre-ignition precursors using spectral information from one of the cylinders in this engine. A custom, simplified borescope was employed to gather visible light from the combustion chamber to detect the glowing pre-ignition precursors. Further, this light signal was sent to an array of four photomultiplier tube (PMT) detectors which were tuned to visible light emission wavelength bands of interest. Three of the four wavelengths (555, 623, 645 nm) were selected to be representative of calcium and CaOH while the fourth wavelength of 715 nm (red emission) was selected to be representative of glowing soot-like aggregates. A high calcium first-generation Dexos™ 1 compliant oil with moderate LSPI activity was used in this study. The analysis revealed that the precursors contained both the signals for calcium as well as the deposit wavelengths in different proportions. More specifically, it was observed that in a series of LSPI events, the first event was usually more biased towards calcium wavelengths suggesting a pre-ignition initiated by the droplet ejecting from the crevice. A relative semi-quantitative metric was developed to compare the light signals for different frequencies to further delve into the pre-ignition mechanism. Two additional fuels were tested with different particulate matter index (PMI) values to assess the impact of PMI on pre-ignition precursors. A bulk comparison of around 30,000 combustion cycles of the two additional test fuels revealed that the high PMI fuel showed more bias towards a deposit induced mechanism while the lower PMI fuel exhibited signs of both droplet and deposit induced pre-ignition precursors.</div></div>

Fuel Effects on Engine-out Emissions Part 2 - Fuel Properties Correlations

<div class="section abstract"><div class="htmlview paragraph">Particulate matter emissions from internal combustion engines have become an increasingly important area of focus for development teams in recent years. This is due to greater regulatory scrutiny on vehicles globally, and especially on particulate emissions. The chemical composition and bulk physical properties of the fuel have been shown to influence the particulate number emissions characteristics. Although some predictive models have been proposed, the causality of specific properties or constituents has not been demonstrated due to the co-linearity of the variables considered in previous studies.</div><div class="htmlview paragraph">In this work, fuels were formulated to capture the expected variation in three key properties of United States (US) market gasoline fuels. Specifically, total aromatics, volatility, and particulate matter index (PMI) were varied across market extremes within regulatory limits--while holding other properties constant. Engine dyno tests were performed with the formulated fuels on a 4-cylinder 2.3 L turbocharged direct injection spark-ignited engine, operated between 2 and 16 bar BMEP.</div><div class="htmlview paragraph">ANOVA analysis was used to demonstrate that both low volatility and high PMI independently increased particle number (PN) emissions at high load at a statistically significant level, whereas total aromatics was only significant at low-load. However, neither low-volatility nor high PMI was universally bad for PN emissions, and other properties also played an important role. For example, at <12 bar BMEP the lowest PN formulated fuel was a low-volatility, high PMI, low aromatics fuel.</div><div class="htmlview paragraph">The nine formulated fuels from this work along with ten fuels tested in Part 1 of this work were used to correlate PN and THC emissions with various fuel properties, within the repeatability of the test. For PN emissions, most of the fuel-to-fuel differences could be explained across the entire load range using a combination of 5 parameters: FBP, T90, A9<sup>+</sup>, IP9<sup>+</sup>, and C11. A9<sup>+</sup> and FBP were always positively correlated with emissions, whereas IP9<sup>+</sup> was negatively correlated, and T90 and C11 switched from negative to positive depending on load. For THC, only three parameters were required, and T70 was by far the best predictor of emissions—accounting for 50-70% of the difference between fuels.</div><div class="htmlview paragraph">The work highlights the critical properties that determine engine out emissions characteristics of different fuels, and shows a path to formulating fuels that can simultaneously reduce PN and THC from vehicles.</div></div>

Fuel Effects on Engine-out Emissions Part 1 - Comparing Certification and Market Gasoline Fuels

<div class="section abstract"><div class="htmlview paragraph">Studies have shown that fuel quality plays an important role in engine-out emissions. The wide variation in composition and properties of gasoline fuels available in the market can lead to discrepancies between the expected emission levels as per set regulations and actual on-road measurements.</div><div class="htmlview paragraph">This study compares engine-out gaseous and particulate emission results between 5 US market fuels, 5 certification fuels and one street-legal race fuel. The market fuels were acquired from different terminals in Michigan. Tests were performed on a 4-cylinder 2.3 L turbocharged direct injection spark-ignited engine. The tests covered a wide range of steady-state operating conditions including load, injection timing and engine speed sweeps. Transient load steps were also performed under warm and cold engine conditions.</div><div class="htmlview paragraph">Weighting factors were assigned to the emissions results from the steady-state operating points in the study; these factors were based upon the frequency of visitation of respective point during an aggressive real drive emission cycle. This was then used to assign a ranking to the fuels.</div><div class="htmlview paragraph">The results from the study indicated that even within the US market-compliant gasoline fuels significant differences existed between the engine out particulate number (PN) and unburned hydrocarbon (HC) emissions, particularly at high load and under cold transient conditions—with more than 200x difference in the PN emissions and 1.5x difference in HC emissions. Furthermore, statistically significant differences existed between the engine out PN emissions of certification and market gasoline fuels, due to differences in their composition and properties. Lastly, significance of higher fuel injection pressures and optimum injection timing was demonstrated to reduce the sensitivity of the engine-out emissions to the fuel type.</div></div>

Prediction of gaseous pollutant emissions from a spark-ignition direct-injection engine with gas-exchange simulation

With the further tightening of emission regulations and the introduction of real driving emission tests (RDE), the simulative prediction of emissions is becoming increasingly important for the development of future low-emission internal combustion engines. In this context, gas-exchange simulation can be used as a powerful tool for the evaluation of new design concepts. However, the simplified description of the combustion chamber can make the prediction of complex in-cylinder phenomena like emission formation quite challenging. The present work focuses on the prediction of gaseous pollutants from a spark-ignition (SI) direct injection (DI) engine with 1D–0D gas-exchange simulations. The accuracy of the simulative prediction regarding gaseous pollutant emissions is assessed based on the comparison with measurement data obtained with a research single cylinder engine (SCE). Multiple variations of engine operating parameters – for example, load, speed, air-to-fuel ratio, valve timing – are taken into account to verify the predictivity of the simulation toward changing engine operating conditions. Regarding the unburned hydrocarbon (HC) emissions, phenomenological models are used to estimate the contribution of the piston top-land crevice as well as flame wall-quenching and oil-film fuel adsorption-desorption mechanisms. Regarding CO and NO emissions, multiple approaches to describe the burned zone kinetics in combination with a two-zone 0D combustion chamber model are evaluated. In particular, calculations with reduced reaction kinetics are compared with simplified kinetic descriptions. At engine warm operation, the HC models show an accuracy mainly within 20%. The predictions for the NO emissions follow the trend of the measurements with changing engine operating parameters and all modeled results are mainly within ±20%. Regarding CO emissions, the simplified kinetic models are not capable to predict CO at stoichiometric conditions with errors below 30%. With the usage of a reduced kinetic mechanism, a better prediction capability of CO at stoichiometric air-to-fuel ratio could be achieved.

Performance of direct injected propane and gasoline in a high stroke-to-bore ratio SI engine: Pathways to diesel efficiency parity with ultra low soot

This work explores pathways to achieve diesel-like, high-efficiency combustion with stoichiometric 3-way catalyst compatible combustion in a single-cylinder spark ignition (SI) research engine. A unique high stroke-to-bore engine design (1.5:1) with cooled exhaust gas recirculation (EGR) and high compression ratio ( rc) was used to improve engine efficiency by up to 30% compared with a production turbocharged gasoline direct injection spark ignition engine. Engine experiments were conducted with both 91 RON E10 gasoline and liquified petroleum gas (LPG) (i.e. autogas) and were compared to legacy gasoline data on the production engine. Geometric compression ratio ( rc) of 13.3:1 was used for both fuels with additional experiments at 16.8:1 for LPG only. Measurements of exhaust soot particle size and number concentrations were made with both fuels. Significant reduction in soot particles across the whole particle size range were achieved with LPG due to the elimination of in-cylinder liquid films. The effects of EGR, late intake valve closing (IVC) and fuel characteristics were investigated through their effects on efficiency, combustion stability and soot production. Results of 47% gross thermal efficiency, and 45% net thermal efficiency at stoichiometric engine operation, at up to 17 bar IMEP and 2000 r/min with 16.8:1 rc were achieved with LPG. Estimated brake efficiency values were compared to a contemporary medium duty diesel engine illustrating the benefits of the chosen path for achieving diesel efficiency parity.

Soot Development in an Optical Direct Injection Spark Ignition Engine Fueled with Isooctane

To better understand the formation and evolution processes of soot, the two-color laser induced incandescence diagnostic method was applied on a single cylinder optical direct injection spark ignition engine. Soot volume fraction was measured, and soot distribution was imaged as cyclic fuel quantity changes. The results show that 45.5 mg/cycle generates the most soot at the same measure plane. Pool fire dominates the formation of soot in the tested engine and generates more soot on the top surface of the piston near the injector. In-cylinder soot increases until 42°CA ATDC and then reduces due to oxidation. Pool fire continues through the expansion stroke till 52°CA ATDC, and then soot cloud gathers near the 10 mm plane. After 82°CA ATDC, in-cylinder soot basically in equilibrium, and residual soot moves follow the in-cylinder flow randomly and evenly distributes within the whole combustion chamber. With increasing cyclic fuel quantity, particles number concentration gradually increases and their distribution present dual-peak shape. In detail, 45.5 mg/cycle emits the most accumulation mode particles while 52 mg/cycle emits the most nucleation mode particles.

PREDICTION OF PERFORMANCE AND EMISSION OF COMPRESSED NATURAL GAS (CNG) IN A SUPERCHARGED DIRECT INJECTION SPARK IGNITION ENGINE USING ARTIFICIAL NEURAL NETWORK

Emissions of greenhouse gases such as carbon dioxide, nitrogen oxide and some hydrocarbons have been the main causes of global warming and have posed severe impacts on climate changes. Consequently, the focus on sustainable developments has swiftly grown in recent years. The utilisation of Compressed Natural Gas (CNG) in the spark ignition (SI) engine or using as dual fuel in compression ignition (CI) engine is getting remarkable attention nowadays. In this paper, performance and emission of CNG in supercharged direct injection spark ignition engine were predicted via Artificial Neural Network (ANN). The Levenberg Marquardt training algorithm was used due to its fast response, easy operation and high accuracy. The models’ results are compared with experimental results available from a previous study by the author. It was observed that R2 values for both performance and emission of CNG were higher than 98%, indicating good prediction. Following the training, high accuracy with value higher than 95% was observed for both analyses. Hence, it can be concluded that ANN is a promising technique for the prediction of performance and emission of CNG in direct injection spark ignition engine that consists of numerous inputs and output conditions.Keywords: Natural gas, CNG, supercharging, artificial neural network

Physicochemical characterization of direct injection Engines's soot using TEM, EDS, X-ray diffraction and TGA

The physical characteristics and elemental composition of particulate matters (PMs) from gasoline direct injection spark ignition (GDI-SI) engines were successfully investigated using transmission electron microscopy - energy dispersive X-ray spectroscopy (TEM-EDS). Thermogravimetric analysis (TGA) was used to analyze the PMs oxidation. The morphology of agglomerated GDI-PMs is not significantly different from the diesel direct injection compression ignition (DDI-CI) engine's PMs. The spherical single primary nanoparticles of the engine's soot composed of curve line carbon crystallites. The average diameter size of the single primary nanoparticles of GDI, DDI, and carbon black are approximately 24 nm, 26 nm, and 31 nm, while the inter-planar spacing is about 0.364 nm, 0.358 nm, and 0.356 nm, respectively. The total fringe lengths of GDI, DDI, and carbon black are approximately 154 nm, 159 nm, and 163 nm measured from the areas of 10 nm × 10 nm inner core regions of primary nanoparticles, and are 180 nm, 195 nm, and 228 nm from the outer shell regions, respectively. The total fringe lengths of inner core are shorter than the outer shell. Besides, the engine's PMs contains both crystalline and amorphous carbon structure using XRD analysis. The GDI-PMs had the least crystalline structure compared to the DDI-PMs and carbon black due to the higher percentage of amorphous fraction. TGA analysis showed that the GDI-PMs oxidation was faster than the DDI-PMs and CB-N330 oxidation because of the primary particle size, the fringe length, and the crystal size which have an impact on oxidation kinetics of particulate matters.