Journal Article


Machine learning for nano-scale particulate matter distribution from gasoline direct injection engine

Abstract

Predicting the amount of combustion generated nano-scale particulate matter (PM) emitted by gasoline direct injection (GDI) is a challenging task, but immensely useful for engine calibration engineers in order to meet the stringent emission legislation norms. The present work aimed to link the in-cylinder combustion with engine-out nano-scale PM for the size range of 23.7 to 1000 nm diameter. Neural network with a single hidden layer using first 8 principal components of cylinder pressure was employed for training and predicting the number of nano-scale PM number count. Using a systematic computational approach and comparing its results with experimental data this work demonstrates that machine-learning approach based on neural network is sufficient for predicting engine out nano-scale PM count as a function of engine load and speed.

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Authors

Pu, Yi-Hao
Keshava Reddy, Jayanth
Samuel, Stephen

Oxford Brookes departments

Faculty of Technology, Design and Environment\Department of Mechanical Engineering and Mathematical Sciences

Dates

Year of publication: 2017
Date of RADAR deposit: 2017-07-04


Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License


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This RADAR resource is the Accepted Manuscript of Machine learning for nano-scale particulate matter distribution from gasoline direct injection engine

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