Time-sequenced flow field prediction in an optical spark-ignition direct-injection engine using bidirectional recurrent neural network (bi-RNN) with long short-term memory. (5th June 2020)

• Record Type: Journal Article
• Authors: Zhao, Fengnian; Ruan, Zhiming; Yue, Zongyu; Hung, David L.S.; Som, Sibendu; Xu, Min
• Is Part Of: Applied thermal engineering. Volume 173(2020)
• Publication Date: 2020-06-05
• Subjects: Optical SIDI engine -- In-cylinder flow prediction -- Bi-RNN model -- Local & global comparison methods; Heat engineering -- Periodicals; Heating -- Equipment and supplies -- Periodicals; Periodicals; 621.40205
• DOI: 10.1016/j.applthermaleng.2020.115253
• Languages: English
• ISSNs: 1359-4311

Abstract

Highlights: Bi-RNN model is used for predicting time-sequenced flow fields in a SIDI engine. Flow structure & magnitude validation are used as model performance criteria. Prediction results of machine learning model matched well with experimental data. The proposed method can improve the temporal resolution in flow measurements. Abstract: To further improve the energy conversion efficiency of internal combustion engine, the transient and complex air flow movement inside the cylinder needs to be better understood and controlled. Although the in-cylinder flow fields are highly stochastic with strong cycle-to-cycle fluctuations, machine learning can still provide an efficient way to learn and regress the complex flow movement process inside the cylinder. In this work, a bidirectional recurrent neural network (bi-RNN) model with long short-term memory was applied to predict the in-cylinder flow fields at different time steps using training data from multi-cycle particle image velocimetry (PIV) measurements. To evaluate the agreement between the true and predicted flow fields, structure and magnitude comparison indices are calculated both globally and locally. The comparison results show that the bi-RNN model can accurately predict the bulk flow and vortex motions from early intake stroke to compression stroke. This work demonstrates that the machine learning model has the potential to predict the underlying dynamics of the interaction between in-cylinder flows and provides a reliable way to improve temporal resolution in PIV flow data to better reveal transient in-cylinder flow features.