scholarly journals Robust Feedback Linearization Approach for Fuel-Optimal Oriented Control of Turbocharged Spark-Ignition Engines

2020 ◽  
Author(s):  
Anh-Tu Nguyen ◽  
Thierry-Marie Guerra ◽  
Jimmy Lauber
Keyword(s):  
Feedback Linearization ◽  
Spark Ignition ◽  
Spark Ignition Engines ◽  
Feedback Linearization Approach

Performances and Emissions Characteristics of Three Main Types Composition of Gasoline-Ethanol Blended in Spark Ignition Engines

2016 ◽  
Vol 10 (7) ◽  
pp. 552
Author(s):  
Marthen E. N. Paloboran ◽  
I. Nyoman Sutantra ◽  
Bambang Sudarmanta
Keyword(s):  
Spark Ignition ◽  
Spark Ignition Engines

scholarly journals Ethanol/Gasoline Blends as Alternative Fuel in Last Generation Spark-Ignition Engines: A Review on CO and HC Engine Out Emissions

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 4034
Author(s):  
Paolo Iodice ◽  
Massimo Cardone
Keyword(s):  
Physicochemical Properties ◽  
Alternative Fuels ◽  
Experimental Studies ◽  
Alternative Fuel ◽  
Exhaust Emissions ◽  
Spark Ignition ◽  
Operating Conditions ◽  
Spark Ignition Engine ◽  
Spark Ignition Engines ◽  
The Impact

Among the alternative fuels existing for spark-ignition engines, ethanol is considered worldwide as an important renewable fuel when mixed with pure gasoline because of its favorable physicochemical properties. An in-depth and updated investigation on the issue of CO and HC engine out emissions related to use of ethanol/gasoline fuels in spark-ignition engines is therefore necessary. Starting from our experimental studies on engine out emissions of a last generation spark-ignition engine fueled with ethanol/gasoline fuels, the aim of this new investigation is to offer a complete literature review on the present state of ethanol combustion in last generation spark-ignition engines under real working conditions to clarify the possible change in CO and HC emissions. In the first section of this paper, a comparison between physicochemical properties of ethanol and gasoline is examined to assess the practicability of using ethanol as an alternative fuel for spark-ignition engines and to investigate the effect on engine out emissions and combustion efficiency. In the next section, this article focuses on the impact of ethanol/gasoline fuels on CO and HC formation. Many studies related to combustion characteristics and exhaust emissions in spark-ignition engines fueled with ethanol/gasoline fuels are thus discussed in detail. Most of these experimental investigations conclude that the addition of ethanol with gasoline fuel mixtures can really decrease the CO and HC exhaust emissions of last generation spark-ignition engines in several operating conditions.

scholarly journals State of Art of Using Biofuels in Spark Ignition Engines

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 779
Author(s):  
Ashraf Elfasakhany
Keyword(s):  
Engine Performance ◽  
Spark Ignition ◽  
Pollutant Emissions ◽  
Spark Ignition Engines ◽  
Engine Emissions ◽  
Cold Starting ◽  
Starting Conditions ◽  
Unburned Hydrocarbons ◽  
Scientific Attention ◽  
State Of Art

Biofuels are receiving increased scientific attention, and recently different biofuels have been proposed for spark ignition engines. This paper presents the state of art of using biofuels in spark ignition engines (SIE). Different biofuels, mainly ethanol, methanol, i-butanol-n-butanol, and acetone, are blended together in single dual issues and evaluated as renewables for SIE. The biofuels were compared with each other as well as with the fossil fuel in SIE. Future biofuels for SIE are highlighted. A proposed method to reduce automobile emissions and reformulate the emissions into new fuels is presented and discussed. The benefits and weaknesses of using biofuels in SIE are summarized. The study established that ethanol has several benefits as a biofuel for SIE; it enhanced engine performance and decreased pollutant emissions significantly; however, ethanol showed some drawbacks, which cause problems in cold starting conditions and, additionally, the engine may suffer from a vapor lock situation. Methanol also showed improvements in engine emissions/performance similarly to ethanol, but it is poisonous biofuel and it has some sort of incompatibility with engine materials/systems; its being miscible with water is another disadvantage. The lowest engine performance was displayed by n-butanol and i-butanol biofuels, and they also showed the greatest amount of unburned hydrocarbons (UHC) and CO emissions, but the lowest greenhouse effect. Ethanol and methanol introduced the highest engine performance, but they also showed the greatest CO2 emissions. Acetone introduced a moderate engine performance and the best/lowest CO and UHC emissions. Single biofuel blends are also compared with dual ones, and the results showed the benefits of the dual ones. The study concluded that the next generation of biofuels is expected to be dual blended biofuels. Different dual biofuel blends are also compared with each other, and the results showed that the ethanol–methanol (EM) biofuel is superior in comparison with n-butanol–i-butanol (niB) and i-butanol–ethanol (iBE).

scholarly journals Modelling combustion in spark ignition engines with special emphasis on near wall flame quenching

2020 ◽  
pp. 146808742097290
Author(s):  
CP Ranasinghe ◽  
W Malalasekera
Keyword(s):  
Combustion Rate ◽  
Fractal Theory ◽  
Pressure Rise ◽  
Spark Ignition ◽  
Spatial Inhomogeneity ◽  
Combustion Model ◽  
Spark Ignition Engines ◽  
Flame Acceleration ◽  
Flame Quenching ◽  
Near Wall

A flame front is quenched when approaching a cold wall due to excessive heat loss. Accurate computation of combustion rate in such situations requires accounting for near wall flame quenching. Combustion models, developed without considering wall effects, cannot be used for wall bounded combustion modelling, as it leads to wall flame acceleration problem. In this work, a new model was developed to estimate the near wall combustion rate, accommodating quenching effects. The developed correlation was then applied to predict the combustion in two spark ignition engines in combination with the famous Bray–Moss–Libby (BML) combustion model. BML model normally fails when applied to wall bounded combustion due to flame wall acceleration. Results show that the proposed quenching correlation has significantly improved the performance of BML model in wall bounded combustion. As a second step, in order to further enhance the performance, the BML model was modified with the use of Kolmogorov–Petrovski–Piskunov analysis and fractal theory. In which, a new dynamic formulation is proposed to evaluate the mean flame wrinkling scale, there by accounting for spatial inhomogeneity of turbulence. Results indicate that the combination of the quenching correlation and the modified BML model has been successful in eliminating wall flame acceleration problem, while accurately predicting in-cylinder pressure rise, mass burn rates and heat release rates.

A Robust Control Design Approach Combining Exact Linearization and High-Gain PI-Observer

2007 ◽  
Author(s):  
Yan Liu ◽  
Dirk So¨ffker
Keyword(s):  
Feedback Linearization ◽  
Control Method ◽  
High Gain ◽  
Observer Design ◽  
Input State ◽  
Exact Linearization ◽  
Practical Applications ◽  
Robust Control Design ◽  
Nonlinear Control Method ◽  
Feedback Linearization Approach

This paper introduces a robust nonlinear control method combining classical feedback linearization and a high-gain PI-Observer (Proportional-Integral Observer) approach that can be applied to control a nonlinear single-input system with uncertainties or unknown effects. It is known that the lack of robustness of the feedback linearization approach limits its practical applications. The presented approach improves the robustness properties and extends the application area of the feedback linearization control. The approach is developed analytically and fully illustrated. An example which uses input-state linearization and PI-Observer design is given to illustrate the idea and to demonstrate the advantages.

Neural network-based output feedback controller for lean operation of spark ignition engines

2006 ◽  
Author(s):  
J.B. Vance ◽  
P. He ◽  
B. Kaul ◽  
S. Jagannathan ◽  
J.A. Drallmeier
Keyword(s):  
Neural Network ◽  
Output Feedback ◽  
Spark Ignition ◽  
Feedback Controller ◽  
Spark Ignition Engines ◽  
Output Feedback Controller
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