Conference Paper

Ensemble empirical mode decomposition for characterising exhaust nano-scale particle emissions of a turbocharged gasoline power unit

Abstract

This paper presents a method for analysing the characteristics of nano-scale particles emitted from a 1.6 Litre, 4-stroke, gasoline direct injection (GDI) and turbocharged spark ignition engine fitted with a three-way catalytic converter. Ensemble Empirical Mode Decomposition (EEMD) is employed in this work to decompose the nano-scale particle size spectrums obtained using a differential mobility spectrometer (DMS) into Intrinsic Mode Functions (IMF). Fast Fourier Transform (FFT) is then applied to each IMF to compute its frequency content. The results show a strong correlation between the IMFs of specific particle ranges and the IMFs of the total particle count at various speed and load operating conditions. Hence, it is possible to characterise the influence of specific nano-scale particle ranges on the total particulate matter signal by analysing the frequency components of its IMFs using the EEMD-FFT method. This approach can provide a useful insight for developing a control strategy for reducing nano-scale particle emissions of a GDI engine. The present work details the systematic methodology followed for using EEMD in combination with FFT to analyse the spectrums of nano-scale particulate matter emissions.

Attached files

Authors

El Yacoubi, Ismail
Samuel, Stephen

Oxford Brookes departments

School of Engineering, Computing and Mathematics

Dates

Year of publication: [not yet published]
Date of RADAR deposit: 2023-08-24

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Related resources

This RADAR resource is the Accepted Manuscript of Ensemble Empirical Mode Decomposition for Characterising Exhaust Nano-Scale Particle Emissions of a Turbocharged Gasoline Power Unit