Experimental Investigation of a Four Stroke Spark Ignition Engine Operated with Naphtha or Gasoline Blended LPG

2016 ◽  
Vol 841 ◽  
pp. 272-277 ◽  
Author(s):  
Samer M. Abdulhaleem ◽  
Hind A. Mohammed
Keyword(s):  
Engine Performance ◽  
Spark Ignition ◽  
Spark Ignition Engine ◽  
Energy Equivalent ◽  
Engine Efficiency ◽  
Unburned Hydrocarbon ◽  
Liquid Petroleum Gas ◽  
Hydrocarbon Emission ◽  
Blended Fuel ◽  
Energy Replacement

In this study experimental work has been conducted using liquid petroleum gas (LPG) as blended fuel with naphtha or gasoline in a spark ignition engine in order to reduce pollutants emissions and to improve engine performance. The fuel blended is done on energy replacement basis which means that the amount of LPG added has an amount of energy equivalent to the energy of naphtha or gasoline removed. The results showed that the LPG blended improve engine efficiency until about 20% blended ratio and reduces CO, CO2, and NOx but causes an increase in unburned hydrocarbon emission. The test carried out at constant compression ratio (6:1), under different load with variety percentage of LPG energy blending ratio of (10-25) %.

scholarly journals A comprehensive numerical study of the ethanol blended fuel effect on the performance and pollutant emissions in spark-ignition engine

2014 ◽  
Vol 18 (1) ◽  
pp. 29-38 ◽  
Author(s):  
Motlagh Zangooee ◽  
Razavi Modarres
Keyword(s):  
Numerical Study ◽  
Load Condition ◽  
Engine Performance ◽  
Spark Ignition ◽  
Spark Ignition Engine ◽  
Pollutant Emissions ◽  
Injection System ◽  
Thermodynamic State ◽  
Point Injection ◽  
Blended Fuel

In the present work, the performance and pollutant emissions in a spark ignition engine has been numerically investigated. For this purpose, the coupled KIVA code with CHEMKIN is used to predict the thermodynamic state of the cylinder charge during each cycle. Computations were carried out for a four cylinder, four strokes, multi point injection system (XU7 engine). Numerical cases have been performed up to 30% vol. of ethanol. Engine simulations are carried out at 2000, 2500 and 3000 rpm and full load condition. The numerical results showed that pollutant emissions reduce with increase in ethanol content. Based on engine performance, the most suitable fraction of ethanol in the blend was found to be nearly 15% for the XU7 engine.

scholarly journals Spark ignition engine performance, standard emissions and particulates using GDI, PFI-CNG and DI-CNG systems

Fuel ◽  
2021 ◽  
Vol 293 ◽  
pp. 120454
Author(s):  
Mindaugas Melaika ◽  
Gilles Herbillon ◽  
Petter Dahlander
Keyword(s):  
Engine Performance ◽  
Spark Ignition ◽  
Spark Ignition Engine ◽  
Performance Standard

scholarly journals Numerical Study of Engine Performance and Emissions for Port Injection of Ammonia into a Gasoline\Ethanol Dual-Fuel Spark Ignition Engine

2021 ◽  
Vol 11 (4) ◽  
pp. 1441
Author(s):  
Farhad Salek ◽  
Meisam Babaie ◽  
Amin Shakeri ◽  
Seyed Vahid Hosseini ◽  
Timothy Bodisco ◽  
...  
Keyword(s):  
Numerical Study ◽  
Combustion Process ◽  
Engine Performance ◽  
Spark Ignition ◽  
Spark Ignition Engine ◽  
Combustion Model ◽  
Dual Fuel ◽  
Spark Ignition Engines ◽  
Performance And Emissions ◽  
Hc Emissions

This study aims to investigate the effect of the port injection of ammonia on performance, knock and NOx emission across a range of engine speeds in a gasoline/ethanol dual-fuel engine. An experimentally validated numerical model of a naturally aspirated spark-ignition (SI) engine was developed in AVL BOOST for the purpose of this investigation. The vibe two zone combustion model, which is widely used for the mathematical modeling of spark-ignition engines is employed for the numerical analysis of the combustion process. A significant reduction of ~50% in NOx emissions was observed across the engine speed range. However, the port injection of ammonia imposed some negative impacts on engine equivalent BSFC, CO and HC emissions, increasing these parameters by 3%, 30% and 21%, respectively, at the 10% ammonia injection ratio. Additionally, the minimum octane number of primary fuel required to prevent knock was reduced by up to 3.6% by adding ammonia between 5 and 10%. All in all, the injection of ammonia inside a bio-fueled engine could make it robust and produce less NOx, while having some undesirable effects on BSFC, CO and HC emissions.

Comparison of Random Forest and Neural Network in Modelling the Performance and Emissions of a Natural Gas Spark Ignition Engine

2021 ◽  
pp. 1-20
Author(s):  
Jinlong Liu ◽  
Qiao Huang ◽  
Christopher Ulishney ◽  
Cosmin E. Dumitrescu
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Natural Gas ◽  
Internal Combustion Engines ◽  
Engine Performance ◽  
Spark Ignition ◽  
Spark Ignition Engine ◽  
Data Driven ◽  
Ann Model ◽  
Mean Square Errors

Abstract Machine learning (ML) models can accelerate the development of efficient internal combustion engines. This study assessed the feasibility of data-driven methods towards predicting the performance of a diesel engine modified to natural gas spark ignition, based on a limited number of experiments. As the best ML technique cannot be chosen a priori, the applicability of different ML algorithms for such an engine application was evaluated. Specifically, the performance of two widely used ML algorithms, the random forest (RF) and the artificial neural network (ANN), in forecasting engine responses related to in-cylinder combustion phenomena was compared. The results indicated that both algorithms with spark timing, mixture equivalence ratio, and engine speed as model inputs produced acceptable results with respect to predicting engine performance, combustion phasing, and engine-out emissions. Despite requiring more effort in hyperparameter optimization, the ANN model performed better than the RF model, especially for engine emissions, as evidenced by the larger R-squared, smaller root-mean-square errors, and more realistic predictions of the effects of key engine control variables on the engine performance. However, in applications where the combustion behavior knowledge is limited, it is recommended to use a RF model to quickly determine the appropriate number of model inputs. Consequently, using the RF model to define the model structure and then employing the ANN model to improve the model's predictive capability can help to rapidly build data-driven engine combustion models.

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