Prediction of hydrogen-added combustion process in T-GDI engine using artificial neural network

Jungkeun Cho, Soonho Song

Research output: Contribution to journalArticle

Abstract

This study investigates the prediction of the combustion process using an artificial neural network (ANN), and an efficient prediction methodology is introduced. In particular, the conditions for using hydrogen as an additive to a turbo-charged gasoline direct injection (T-GDI) engine are discussed. Research to predict the physical phenomena using ANNs has been actively conducted for various applications, including internal combustion engines. However, a large amount of data must be collected under various conditions to establish these predictions. Furthermore, the prediction of complex phenomena such as engine-combustion processes mandates data collection under diverse conditions. It is therefore very costly and time-consuming to obtain these data experimentally under a wide range of conditions. However, the methodology introduced in this study can enable effective prediction of complex combustion processes, such as hydrogen-added combustion, with minimal experimental data. To implement this methodology, the target engine was modeled using commercial 1D engine-simulation software GT-Power based on certain experimental results obtained under select conditions. The data for the ANN training under an expanded range of conditions were obtained using the GT-Power engine model. According to the obtained data, the ANN model for prediction of the hydrogen-added combustion processes in the T-GDI engine was constructed, and its results were compared with the experimental results. A reasonable agreement between the compared results was observed, which demonstrated the validity and reliability of the prediction model. The constructed ANN combustion model has the potential that it can be applied to transient conditions or used as a virtual sensor, unlike general combustion models, and this study presented an economical and efficient way to build such a model.

Original languageEnglish
Article number115974
JournalApplied Thermal Engineering
Volume181
DOIs
Publication statusPublished - 2020 Nov 25

All Science Journal Classification (ASJC) codes

  • Energy Engineering and Power Technology
  • Industrial and Manufacturing Engineering

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