Machine learning for nano-scale particulate matter distribution from gasoline direct injection engine. (October 2017)

• Record Type: Journal Article
• Authors: Pu, Yi-Hao; Keshava Reddy, Jayanth; Samuel, Stephen
• Is Part Of: Applied thermal engineering. Volume 125(2017)
• Page Start: 336
• Page End: 345
• Publication Date: 2017-10
• Subjects: Nano-scale particulate matter -- Gasoline direct injected engine -- Machine learning -- Principal component analysis; Heat engineering -- Periodicals; Heating -- Equipment and supplies -- Periodicals; Periodicals; 621.40205
• DOI: 10.1016/j.applthermaleng.2017.07.021
• Languages: English
• ISSNs: 1359-4311

Abstract

Highlights: Machine learning is used for predicting Nanoscale particle count in GDI engine. A Single hidden for predicting particle count of 23–1000 nm diameter is sufficient. Valid approach for developing engine calibration for engine-out nano-scale PM count. 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 23.7–1000 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.