Spark Ignition Engine Research Articles

Experimental study on ignition characteristic and performance of a two-stroke engine fueled with aviation kerosene

Two-stroke spark ignition engines are widely used in Unmanned Aerial Vehicles. Aviation kerosene offers advantages over aviation gasoline due to its higher flash point and lower volatility, making it likely to be adopted more widely in the future. However, the poor evaporation and atomization characteristics of aviation kerosene result in suboptimal ignition performance, especially in the engine start-up phase. To explore the fitting mixture concentration and temperature conditions for achieving better ignition performance of aviation kerosene, A series of experimental studies are conducted on a two-stroke engine. The intrinsic factors affecting engine performance are analyzed. The experimental results indicate that optimal ignition characteristics, maximum power output, and a reduced misfire rate can be achieved at mixture concentration of 0.6 and mixture temperature of 80?C. Furthermore, the misfire rate is most sensitive to the combustion duration, while the cyclic variations show significant sensitivity to ignition delay. The results provide guidance for optimizing ignition performance during the start-up phase of two-stroke engines fueled with aviation kerosene.

Ammonia-methanol and ammonia-ethanol dual-fuel combustion in an optical spark-ignition engine: A multiple flame generation approach

Ammonia-methanol and ammonia-ethanol dual-fuel combustion in an optical spark-ignition engine: A multiple flame generation approach

Effect of vehicle operating parameters on fuel consumption and the overall energy efficiency of the drive system

This article presents a research methodology that supplements known studies to solve the problem of a correct complementary analysis of vehicles with different drive units. This method can be used at the stage of selecting a car for specific tasks, as well as for in-service technical condition checks. A new method is proposed for analysing the impact of operating conditions on the mileage fuel consumption, unit fuel consumption and overall energy efficiency of vehicles. In the study, it was possible to determine the effect of changing the vehicle speed, road gradient angle and vehicle weight. Tests identifying fuel consumption and efficiency characteristics as a function of vehicle load were carried out using passenger cars with different drive designs. Carrying out tests under laboratory conditions on a chassis dynamometer bench enabled the precise determination of the change in operating conditions. The criteria adopted for the evaluation included fuel consumption and overall vehicle energy efficiency. The variable parameters included speed, vehicle weight, and the road gradient angle. The data for criteria calculation were acquired using a diagnostic tester with a functional parameter recording function. The application of the presented method enabled a comparison of the overall energy efficiency of two vehicles equipped with spark-ignition combustion engines, according to the criteria listed. The experiment showed that in a three-cylinder engine, unit fuel consumption is more sensitive to parameter changes under identical conditions (speed, vehicle weight, road gradient angle) than in a four-cylinder engine. To evaluate the drive units of the test vehicles, characteristics of changes in the overall energy efficiency were used as well. It was observed that in all testing variants, the value increased with increasing road gradient values, with the intensity of η increase not being constant. A car with a four-cylinder engine has a higher energy efficiency at low loads. The proposed methodology is relevant for evaluating vehicles with different drive systems (including hybrid) and adapting drive characteristics to the operating conditions. Partially the presented methodology can also be transferred to EV vehicles - in terms of energy efficiency.

Theoretical Analysis of Performance Characteristics of Non- road Spark Ignition Engines

Non-road spark ignition (SI) engines play a critical role in Nigeria, particularly in agriculture, construction, and small-scale power generation. Despite their extensive use, there is limited research on how Nigeria's unique environmental and operational conditions such as high ambient temperatures, poor fuel quality, and irregular maintenance practices affect the performance of these engines. This study presents a theoretical analysis of the performance characteristics of non-road SI engines under Nigerian conditions, focusing on fuel efficiency, power output, thermal efficiency, and emissions. The effect of engine speed, engine load and equivalence ratio on the performance characteristics of non-road spark ignition engines were studied in this research. Three operating parameters (engine load, engine speed, and equivalence ratio) and eight engine performance parameters which are Brake Specific Fuel Consumption (BSFC), Brake Power (BP), Thermal Efficiency (TE), Brake Mean Effective Pressure, Exhaust Gas Temperature (EGT), carbon monoxide emission, hydrocarbon emission, and nitrogen oxide were considered in this work. Theoretical analysis was performed using a two-zone SI engine model, the resulting equations were solved with the aid of a computer program developed by the authors and implemented in MATLAB software. The effect of the operating parameters on the performance parameters was simulated using the computer code developed which was implemented in MATLAB. The results show that the engine speed of 3000 rpm gave the optimum values of 250 g/kWh, 33 kW, 1070 kPa, 28% and 720 oC for BSFC, BP, BMEP, TE, and EGT respectively. Also, all performance parameters increased with an increase in engine load. The equivalence ratio of 0.9 gave the optimum values for all the parameters considered in this work. It can be concluded that for optimum performance of non-road SI engines, it should be operated at equivalence ratio of 0.9, engine speed of 3000 rpm and low engine loads.

THE VOLUMETRIC EFFICIENCY OF A SINGLE-CYLINDER SPARK-IGNITION ENGINE AS AFFECTED BY AIR INTAKE TEMPERATURE AND HUMIDITY

Nowadays, there are an increasing demand for energy efficiency and emission reduction by the internal combustion engine. It can be achieved by improving the volumetric efficiency of spark ignition engine. The air intake temperature and humidity give an impact to the volumetric efficiency. Thus, the goal of this study is to investigate the impacts of air intake temperature and humidity on the volumetric efficiency of a single cylinder spark ignition engine. This study was facilitated using Altair HyperStudy. It was executed by full factorial design of experiments (DOE). The effects of engine speed, air intake temperature, and humidity towards the volumetric efficiency was assessed based on one-dimensional engine simulation. The ANOVA analysis showed a positive correlation between engine speed and volumetric efficiency. There is a negative effect found for the air intake temperature. However, a negligible impact of humidity obtained in this study. The optimal configuration for achieving a maximum volumetric efficiency of 0.97 was identified at 4500 RPM, 26 degrees Celsius, and 68.0% humidity. It is suggested by the results that while engine speed has a significant impact on volumetric efficiency, temperature and humidity also play important roles in optimizing engine performance. These insights are valuable for designing and operating more efficient and environmentally friendly engines.

Experimental and numerical investigation of abnormal combustion phenomena in high-performance hydrogen direct-injection engine operated in stochiometric conditions

Nowadays the use of Hydrogen (H2) within Internal Combustion Engine (ICE) represents a valuable solution also for high-performance applications since it allows carbon free combustion process preserving, at the same time, the driving experience of the engines fuelled with conventional fuels. On the other side, the low ignition energy and the high flammability range may lead to a high probability of abnormal combustion events especially at high engine loads. Therefore, this work aims at investigating the occurrence of knock and pre-ignition phenomena in a high-performance Direct Injection (DI) single cylinder Spark Ignition (SI) engine through the synergistic use of numerical simulations and experimental activities. A 3D-CFD model was calibrated against a set of experimental measurements. The engine model showed satisfactory predictive capabilities with a good matching with the experimental in-cylinder pressure traces. This virtual test rig was then used to investigate the impact of a different coolant temperature and different injector recess in terms of knock and pre-ignition tendency, and most of all, to define a robust simulation methodology to estimate the risk of hydrogen abnormal combustion events.

A Review of Ammonia Combustion and Emissions Characteristics in Spark-Ignition Engines and Future Road Map

Ammonia (NH3) is gaining recognition as a viable “green” transportation fuel due to its zero-carbon characteristic, its high energy density and its widespread availability. However, NH3 has a high auto-ignition temperature, resulting in potential emissions of NOx and unburned NH3. Addressing combustion challenges requires innovative solutions, such as the application of combustion promoters to enhance NH3 combustibility. This review article focuses on the compatibility of NH3 as a fuel for spark-ignition (SI) engines, examining its combustion under various modes including pure NH3 combustion, gasoline blends, NH3/hydrogen (H2) blends, and NH3/natural gas blends in single or dual-fuel configurations. The formation of nitrogen oxides (NOx) and slip-NH3 is explored to understand emissions species such as NO and N2O. Additionally, the article highlights the limitations of NH3 as a fuel for SI combustion. The comprehensive discussion provided in this review aims to fill a critical gap in the literature regarding NH3’s feasibility as a zero-carbon fuel for SI engines, particularly in the maritime sector. By offering insights into NH3 combustion characteristics and emissions profiles, the article seeks to provide a roadmap for leveraging NH3 as a suitable non-carbon fuel to decarbonize the marine sector and advance global sustainability goals.

Influences of ternary ethanol methanol gasoline mixtures on the performance of a spark ignition engine

The principal aim of this study is to determine the influence of ternary mixtures of gasoline, ethanol and methanol as fuels, on a spark ignition engine performance. Four samples of fuels were prepared with varying concentrations of each constituent. The different fuels studied are: EM0 (gasoline), EM5 (2.5% ethanol, 2.5% methanol, 95% gasoline), EM10 (5% ethanol, 5% methanol, 90% gasoline) and EM15 (7.5% ethanol, 7.5% methanol, 85% gasoline). A battery of tests (appearance, color, odor, lower heating value, density, research octane number, vapor pressure, total sulfur content and distillation curve) was carried out on these mixtures in order to determine their physicochemical properties and their ability to be used as fuel. Then, the different prepared mixtures were used as fuels to determine the performance of the 4-stroke, carbureted Renault 4 engine. These tests show that these additions give rise to a fuel with a lower sulfur level, a higher RON and a lower low heating value compared to unleaded gasoline and therefore improve, depending on the circumstances, the power of the fuel engine, its specific consumption, its speed and combustion. At the end of this study, it was concluded that the use of fuel mixtures was relevant for high speeds and also more suitable for total loads.

Ammonia Combustion: Internal Combustion Engines and Gas Turbines

The quest for renewable energy sources has resulted in alternative fuels like ammonia, which offer promising carbon-free fuel for combustion engines. Ammonia has been demonstrated to be a potential fuel for decarbonizing power generator, marine, and heavy-duty transport sectors. Ammonia’s infrastructure for transportation has been established due to its widespread primary use in the agriculture sector. Ammonia has the potential to serve as a zero-carbon alternative fuel for internal combustion engines and gas turbines, given successful carbon-free synthesis and necessary modifications to legacy heat engines. While its storage characteristics surpass those of hydrogen, the intrinsic properties of ammonia pose challenges in ignition, flame propagation, and the emissions of nitrogen oxides (NOx) and nitrous oxide (N2O) during combustion in heat engines. Recent noteworthy efforts in academia and industry have been dedicated to developing innovative combustion strategies and enabling technologies for heat engines, aiming to enhance efficiency, fuel economy, and emissions. This paper provides an overview of the latest advancements in the combustion of neat or high-percentage ammonia, offering perspectives on the most promising technical solutions for gas turbines, spark ignition, and compression ignition engines.

Investigating the relationship between heating value and combustion/emissions and residual gas in a small spark-ignition engine powered by pure propane

Investigating the relationship between heating value and combustion/emissions and residual gas in a small spark-ignition engine powered by pure propane

Exploration of Engine Parameters for Emission Reduction in Gasoline-Ethanol Fueled Engines

The main objective of this study is to develop spark ignition engine parameters that allow complete combustion while reducing dependence on fossil fuels. To achieve this goal, optimization of compression ratio, gasoline-ethanol mixture, ignition timing, and spark plug type was used. In addition, this study used water injection that continuously injects water before the intake manifold. In this study, the Taguchi method with the L9 orthogonal array was applied. According to the experimental verification results, the best combination to reduce exhaust emission levels is to utilize gasoline-ethanol (E70), a compression ratio (CR) of 15.6:1, an ignition degree of +4°, and a platinum spark plug. Meanwhile, the presence of water injection at 1.45 ml/s helps reduce vehicle exhaust pollutants.

Experimental verification on the effectiveness on hydrogen peroxide fuel blends in gasoline engine operation: Exhaust emission analysis

This experiment aims to investigate the effectiveness of hydrogen peroxide on the exhaust emission characteristics of a spark ignition engine. 5%, 10%, and 15% of hydrogen peroxide-gasoline mixtures are the fuel blends used in the current study. Experimental results about emission characteristics of spark ignition fueled with hydrogen peroxide-gasoline blend at an engine speed of 2500RPM with variable loads are presented in detail. The results are then compared to that of gasoline fuel operations. Hydrogen peroxide-gasoline blends indicate lower CO and SO2 emissions. The most significant penalty will be a slight increase in EGT, HC, and NOx emissions. It is concluded that a 10% hydrogen peroxide-gasoline fuel blend is the optimum blend for acceptable emission controls.

Enhancing HCCI Engine Performance through AI Integration: Addressing Ignition Timing and Emission Challenge

This article explores the integration of artificial intelligence (AI) with Homogeneous Charge Compression Ignition (HCCI) engines to address the inherent drawbacks of traditional spark-ignition (SI) and compression-ignition (CI) engines. It highlights how HCCI technology mitigates issues such as higher emissions and lower efficiency associated with SI and CI engines. However, HCCI also faces challenges, particularly in ignition timing control. The article delves into the detrimental impact of diesel emissions on engines and underscores the critical role of precise ignition timing in HCCI performance. To overcome these challenges, the potential of combining AI, specifically through the Internet of Things (IoT) and deep learning within the realm of machine learning, is examined. The integration of AI technologies with HCCI engines promises significant improvements in thermal efficiency and fuel economy by optimizing ignition timing. This synergy between AI and HCCI engines represents a promising avenue for enhancing engine performance while reducing environmental impact.

Performance improvement with boosted intake pressure and hydrogen enriched biogas and producer gas-fuelled SI engine

The depletion of conventional fuels and rising energy demand necessitate alternative energy solutions, with biomass emerging as a viable resource. However, relying on a single renewable source poses challenges like inconsistent supply and reduced engine power output. This study numerically simulates a spark-ignition (SI) engine fueled by a triple fuel blend of producer gas (PG), biogas (BG), and hydrogen (HG). A comprehensive quasi-dimensional thermodynamic model, coded in FORTRAN, simulates SI engine performance by varying input factors such as biogas blending ratio, intake pressure, and late inlet valve closure timing (LIVC). This study introduces a novel approach to simulate the combined impact of triple fuel blending concentration, intake boost pressure, and inlet valve closure timing on SI engine performance and emissions. Additionally, it aims to identify optimal operating parameters to maximize power and efficiency while minimizing fuel consumption and emissions. Using response surface methodology and an analysis of variance regression model, results showed statistical significance at a 95 % confidence level. Optimal conditions were found at 82.99° aBDC for LIVC timing, 23.28 % biogas concentration, and 1.99 bar inlet pressure. Under these conditions, the engine achieved 2.39 kW brake power, 28.92 % brake thermal efficiency, 12,461 kJ/kWh brake-specific energy consumption, 0.081 % carbon monoxide, and 1640.36 ppm nitric oxide, with a composite desirability of 0.843. In triple fuel blend, while biogas concentration changes from 40 % to 23.28 % with increasing the intake pressure from 1 to 2 bar, BTE improves from 22.24 to 28.92 %. These results demonstrate that simulation and optimization can effectively predict SI engine performance with blended gaseous fuels.

A Comparative Analysis of Advanced Machine Learning Models for the Prediction of Combustion, Emission and Performance Characteristics Using Endoscopic Combustion Flame Image of a Pine Oil–Gasoline Fuelled Spark Ignition Engine

This research focuses on using machine learning to predict the spark ignition engine’s combustion, performance, and emission parameters with bio-fuel blends such as pine oil blend, which significantly diminishes the environmental impact of traditional fuels, reduces the limitations of repeated engine experimentation and addresses the nonlinearities in engine test results contributing to sustainable cleaner fuel and energy solutions. The models used were Ensemble Decision Tree Bagging, Ensemble Least Squares Boosting, Gaussian Process Regression and Support Vector Machine Regression, with good generalization ability. Brake Specific Fuel Consumption data from the test engine trials and endoscopic image flame area data after spark timing at different crank angles (320, 400, 480, 560, and 640 after Spark Timing) were fed into the machine-learning models as predictors. The response variables were Brake thermal efficiency, Unburnt Hydrocarbons, Carbon monoxide, Carbon dioxide, Oxides of nitrogen, maximum In-cylinder pressure, and maximum heat release rate. The bootstrap technique was used to generate numerous datasets from the experimental data for data-driven model training and tested using both interpolative and extrapolative data. The experimental and predicted values for all these algorithms were subjected to repeated hyperparameter optimization trials and the best machine learning method was identified using the performance and error metrics. The Ensemble Least Squares Boost model showed the overall best correlation (R2) in the range of 0.97 - 0.99 for gasoline and pine oil PN20 blend for the predicted versus experimental engine parameters. The root-mean-squared error (RMSE) at maximum load ranged between 0.0086 - 0.3044 for gasoline and 0.0049 - 0.2046 for the Pine oil PN20 fuel blend respectively. Therefore, employing an Ensemble Least Squares Boosting machine learning framework can effectively predict the characteristics of gasoline engines using pine oil and blends. This approach serves as a virtual engine model, efficiently overcoming the limitations and complexities inherent in conventional engine experiments.

Analyzing the impact of injection timing variations on the performance of multi-cylinder CNG direct injection spark-ignition engine

Abstract Objective: This study investigates the impact of Compressed Natural Gas (CNG) injection timing on the performance of a retrofitted CNG Direct Injection (CNG-DI) engine. The aim is to understand how varying injection timing affects key performance metrics such as torque, power, thermal efficiency, and fuel consumption in CNG-DI engines, providing insights for system optimization.
Methods: A 1298 cc, four-cylinder, four-stroke spark ignition engine with a compression ratio of 9 was retrofitted for CNG-DI operation. Modifications included a redesigned cylinder head for high-pressure injectors, controlled by a programmable gas ECU. The engine was tested with three injection timings: Early Injection (EI) at 230° BTDC, Optimal Injection (OI) at 180° BTDC, and Late Injection (LI) at 130° BTDC. Performance, Emission and Combustion parameters were evaluated across engine speeds from 1000 to 3100 rpm.
Results: Late Injection (LI) timing improved torque, power, BMEP, thermal efficiency, and volumetric efficiency by 4-16% within the 1000-2200 rpm range, attributed to better combustion dynamics. Optimal Injection (OI) timing achieved 4-12% higher performance in the 2500-2800 rpm range due to improved mixture formation. Early Injection (EI) timing resulted in 3-8% improvements at speeds above 2800 rpm, driven by faster combustion and increased peak power.
Conclusion: This study highlights the crucial role of injection timing in optimizing CNG-DI engine performance: Late Injection improves fuel efficiency at lower speeds, Optimal Injection enhances thermal efficiency at mid-range RPMs, and Early Injection maximizes power at higher RPMs. However, the study is limited by its focus on a single engine configuration and does not consider emissions or Life Cycle Assessment (LCA). Future research should investigate the environmental impact of injection timing through LCA and assess the long-term durability of engine performance.

Thermodynamics-based data-driven combustion modelling for modern spark-ignition engines

Combustion modelling is complicated, computationally expensive, and crucial for the development of modern spark-ignition (SI) engines. This study introduces a novel data-driven approach to improve the predictability of phenomenological SI engine models. First, a physics-based model is used to generate Mass Fraction Burned (MFB) profiles for 1,258 precisely controlled knock-limited combustion experiments. To predict these MFB profiles based on the operating conditions, Artificial Neural Networks (ANN), Multiple Output Support Vector Regression (MOSVR), and Multivariate Gaussian Process (MGP) are then applied. Among these, MGP demonstrates superior performance due to the Gaussian-like distribution of the outputs. Further sensitivity analysis using MGP identifies critical inputs that are not engine specific to develop a thermodynamics-based data-driven model. The model demonstrates high accuracy, uses normalised inputs that are independent of engine geometry, and consistently performs well with small datasets. When applied to a different but similarly sized engine, the model accurately predicts the knock-limited spark timing and captures the MFB profile relatively well, showing strong generalisability. This study not only improves the predictability of engine combustion simulations but also establishes a valuable dataset for further development of data-driven models in different engines.

Artificial Intelligence based Emission and Performance Prediction, and Optimization of HHO-Blended Gasoline SI Engine: A Sustainable Transition

In striving for sustainable alternatives to gasoline, Oxyhydrogen (HHO) has emerged as a promising substitute for Internal Combustion Engines (ICEs). HHO blends not only improve engine efficiency but also reduce harmful emissions. On-site, HHO utilization in the engine, eradicates low energy density and storage challenges. The current study combined cutting-edge machine learning (ML) techniques like Artificial Neural Network (ANN) and Gradient-based optimization to effectively utilize HHO with gasoline. Experimentation involved a single-cylinder spark ignition (SI) engine fueled by varying HHO-gasoline blends across different loads and speeds. Iterative tuning of the loss function led to the identification of the optimal architecture, denoted as 2HL-10N (2 hidden layers with 10 neurons each), with impressive correlation coefficients (0.99481 for training, 0.9781 for validation, 0.96914 for testing, and overall, 0.98819). Subsequently, ANN led Gradient-based optimization unveiled key performance metrics along with emissions. Upon implementing optimized conditions (HHO: 3.78 l/m, load: 100%, and 3465 rpm), notable enhancements were observed. The torque and efficiency increased by 11.8%, and 7.1%, respectively. Furthermore, brake-specific fuel consumption, carbon monoxide, and hydrocarbon emissions showed a reduction of 11.5%, 27.1%, and 36.6%, respectively. ANN based optimal engine operation revealed HHO as a potential replacement for conventional gasoline.

Exploration the effect of natural gas composition on exhaust emissions of a CNG SI (spark-ignition) engine by extreme N2 substitution

Considering the increasingly tougher emission regulatory, applications of compressed natural gas (CNG) fuel in internal combustion engine must be more and more careful because composition of CNG fuel changes by regions and seasons. Composition of CNG fuel has important impact on engine exhaust emissions, many works in literature have been done and a big knowledge base has been built. But most previous studies directly changed the CNG composition during investigation. In this work, we only used CH4 and N2 to simulate the extreme CNG composition range (only the heat value of fuel was changed). Then the gaseous and particle emissions of a CNG engine were comparatively investigated between pure CNG and 13.6% N2 substitution CNG (denoted as N2-CNG) under World Harmonized Transient Cycle (WHTC). Results showed that the corrected fuel consumption (corrected by N2 substitution proportion) of N2-CNG was 258.4 g/kW·h which was slightly lower than that of Pure-CNG (263.8 g/kW·h), under N2-CNG operation, per unit weight fuel generated higher work. So, HC and CO emissions of N2-CNG were lower relative to Pure-CNG. CH4 and CO emission factors were reduced by 32% and 3.1%, respectively, by extreme N2 replacement, while N2-CNG emitted higher NOx relative to Pure-CNG; with three way catalyst (TWC), CH4 was decreased by 88.5%, and other species were reduced by about 99%, the difference of pollutant conversion between two fuels was ignorable. Although particle number emitted by N2-CNG was higher, the weight of PM emission of N2-CNG was lower than that of Pure-CNG, illustrating that the mean diameter of PM emitted from N2-CNG was smaller relative to Pure-CNG. And NOx and PM emissions of CNG engine were lower than by 2-3 orders of magnitude than diesel engine. Then, the transient emissions were comparatively analyzed by combining evolutions of engine condition, exhaust temperature and fuel rate. This study is anticipated to provide a good demonstration for better usage of NG fuels in traditional engines.

Analytical, Computer and Laboratory Modelling of the Effect of the Fuel used in the Spark Ignition Engine of the Selected Vehicle on the Operating Parameters and Exhaust Gas Composition

Analytical, Computer and Laboratory Modelling of the Effect of the Fuel used in the Spark Ignition Engine of the Selected Vehicle on the Operating Parameters and Exhaust Gas Composition