A Machine Learning (ML) approach is presented to correlate in-cylinder images of early flame kernel development within a spark-ignited (SI) gasoline engine to early-, mid-, and late-stage flame propagation. The objective of this study was to train machine learning models to analyze the relevance of flame surface features on subsequent burn rates. Ultimately, an approach of this nature can be generalized to flame images from a variety of sources. The prediction of combustion phasing was formulated as a regression problem to train predictive models to supplement observations of early flame kernel growth. High-speed images were captured from an optically accessible SI engine for 357 cycles under pre-mixed operation. A subset of these images was used to train three models: a linear regression model, a deep Convolutional Neural Network (CNN) based on the InceptionV3 architecture and a CNN built with assisted learning on the VGG19 architecture. Analysis of these models showed that images of the early flame in the combustion cycle do contain information to train regression and CNN models on forthcoming states (i.e., CA10, CA50); however, there were significant limitations on how far into the burn process these predictions remained accurate due to the complex thermal physics (i.e., CA90). Future research should address the need to increase the quantity of training data or to introduce additional measurements, such as simultaneous images from multiple camera angles and flame propagation late in the combustion cycle.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Prediction of Combustion Phasing Using Deep Convolutional Neural Networks


    Additional title:

    Sae Technical Papers


    Contributors:

    Conference:

    WCX SAE World Congress Experience ; 2020



    Publication date :

    2020-04-14




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




    Prediction of Combustion Phasing Using Deep Convolutional Neural Networks

    Johnson, Ryan / Kaczynski, David / Zeng, Wei et al. | British Library Conference Proceedings | 2020


    Prediction of Combustion Phasing Using Deep Convolutional Neural Networks

    Johnson, Ryan / Kaczynski, David / Zeng, Wei et al. | British Library Conference Proceedings | 2020



    Prediction of Engine-Out Emissions Using Deep Convolutional Neural Networks

    Warey, Alok / Gao, Jian / Grover, Ronald | SAE Technical Papers | 2021


    Prediction of Engine-Out Emissions Using Deep Convolutional Neural Networks

    Warey, Alok / Gao, Jian / Grover, Ronald | British Library Conference Proceedings | 2021