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Spark-ignition engine

About: Spark-ignition engine is a research topic. Over the lifetime, 4352 publications have been published within this topic receiving 66550 citations.


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Journal ArticleDOI
TL;DR: In this paper, an artificial neural network model was developed to predict the engine performance and exhaust emissions when a port fuel injection spark ignition engine fueled with n-butanol-gasoline blends under various equivalence ratio.
Abstract: The engine experiments require multiple tests that are hard, time-consuming, and high cost. Therefore, an artificial neural network model was developed in this study to successfully predict the engine performance and exhaust emissions when a port fuel injection spark ignition engine fueled with n-butanol–gasoline blends (0–60 vol.% n-butanol blended with gasoline referred as G100-B60) under various equivalence ratio. In the artificial neural network model, compression ratio, equivalence ratio, blend percentage, and engine load were used as the input parameters, while engine performance and emissions like brake thermal efficiency, brake-specific fuel consumption, carbon monoxide, unburned hydrocarbons, and nitrogen oxides were used as the output parameters. In comparison between experimental data and predicted results, a correlation coefficient ranging from 0.9929 to 0.9996 and a mean relative error ranging from 0.1943% to 9.9528% were obtained. It is indicated that the developed artificial neural network ...

32 citations

Proceedings ArticleDOI
TL;DR: A cycle-resolved two-dimensional flame visualization technique using Mie-scattering from submicron sized smoke particles added to the homogeneous charge mixture of a spark-ignition engine has been developed as discussed by the authors.
Abstract: A cycle-resolved two-dimensional flame visualization technique using Mie-scattering from submicron sized smoke particles added to the homogeneous charge mixture of a spark-ignition engine has been developed. This diagnostic technique was applied to a square piston engine with four windows. Pulsed laser sheets were generated by a copper vapor laser at a frequencies of 6 kHz. The light scattered by the smoke particles was collected by a drum camera on high sensitivity photographic film.

32 citations

Journal ArticleDOI
15 Feb 2018-Fuel
TL;DR: In this article, the laminar burning velocity of water/ethanol/air mixtures with up to 40% water by volume was measured with a constant volume combustion vessel using two distinct techniques: imaging of the flame front during the constant pressure period and analyzing the pressure rise data.

32 citations

Proceedings ArticleDOI
TL;DR: In this paper, an approach to SA mapping is presented, with the objective of improving the performance analysis robustness, while reducing the test time, based on the observation that, for a given running condition, IMEP can be considered a function of the combustion phasing, represented by the 50% Mass Fraction Burned (MFB50) parameter.
Abstract: Engines performance and efficiency are largely influenced by the combustion phasing. Operating conditions and control settings influence the combustion development over the crankshaft angle: the most effective control parameter used by Electronic Control Units (ECU) to optimize the combustion process for Spark Ignition (SI) engines is Spark Advance (SA). SA mapping is a time-consuming process, usually carried out with the engine running in steady state on the test bench, changing SA values while monitoring Brake and Indicated Mean Effective Pressure (BMEP, IMEP) and Brake Specific Fuel Consumption (BSFC). Mean values of IMEP and BSFC for a test carried out with a given SA setting are considered as the parameters to optimize. However, the effect of SA on IMEP and BSFC is not deterministic, due to the cycle-to-cycle variation: the analysis of mean values requires many engine cycles to be significant of the performance obtained with the given control setting. Finally other elements, such as engine or components ageing, and disturbances like Air-to-Fuel Ratio (AFR) or air, water and oil temperature variations, could affect the tests results: this facet can be very significant for racing engines testing. This paper presents a novel approach to SA mapping, with the objective of improving the performance analysis robustness, while reducing the test time. The methodology is based on the observation that, for a given running condition, IMEP can be considered a function of the combustion phasing, represented by the 50% Mass Fraction Burned (MFB50) parameter. Due to cycle-to-cycle variation, many different MFB50 and IMEP values are obtained during a steady state test carried out with constant SA. While MFB50 and IMEP absolute values are influenced by disturbance factors, the relationship between them holds, and it can be synthesized by means of the angular coefficient of the tangent line to the MFB50-IMEP distribution. The angular coefficient variations as a function of SA can be used to feed a SA controller, able to maintain the optimal combustion phasing. Similarly, knock detection is approached by evaluating two indexes: the distribution of a typical knock-sensitive parameter (MAPO, Maximum Amplitude of Pressure Oscillations) is related to that of CHRNET (net Cumulative Heat Release), determining a robust knock index. A knock limiter controller can then be added, in order to restrict the SA range to safe values. The methodology can be implemented in real-time combustion controllers: the algorithms have been applied offline to sampled data, showing the feasibility of fast and robust automatic mapping procedures.Copyright © 2009 by ASME

32 citations

Journal ArticleDOI
TL;DR: In this paper, the performance of small biogas-fuelled engines and high-efficiency strategies for power generation in the very low power range of less than 1000 W were evaluated.

32 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202388
2022168
2021201
2020168
2019211
2018211