A data mining study on combustion dynamics and NOx emission of a swirl stabilised combustor with secondary fuel injection. (2017)

• Record Type: Journal Article
• Authors: Riazi, Rouzbeh; Asrardel, Mohamad; Shafaee, Maziar; Vakilipour, Shidvash; Zare, Hadi; Veisi, Hadi
• Is Part Of: International journal of heavy vehicle systems. Volume 24:Number 3(2017)
• Page Start: 215
• Page End: 238
• Publication Date: 2017
• Subjects: swirl-stabilised combustor -- secondary fuel injection -- pressure fluctuation -- emission index -- MPR -- multiple polynomial regression -- neural network; Motor vehicles -- Design and construction -- Periodicals; Commercial vehicles -- Design and construction -- Periodicals; Motor vehicles -- Design and construction; Periodicals; 629.224
• Languages: English
• ISSNs: 1744-232X

Abstract

To study the relations between the amounts of NOx emission, noise level, and level of pressure fluctuations as the output quantities of an experimental swirl-stabilised combustor and two variables of overall equivalence ratio (ϕ) and secondary fuel injection rate (Qsec ), as its input quantities, two different data mining approaches were employed in the present work (i.e., artificial neural network (ANN) and multiple polynomial regression (MPR) techniques). The related experiments were already carried out using four different types of secondary fuel injectors with an overall equivalence ratio (ϕ) in the range of 0.7~0.9. The results indicate that both the ANN and MPR methods have lower predicting capability for estimation of noise level and the level of pressure fluctuations compared with that of the emission index. Also the results show that the ANN has better predicting capability, for estimation of various combustor parameters, than the MPR method.