Machine Learning of ignition delay times under dual-fuel engine conditions

Dual-fuel (DF) compression ignition engines, which employ a high-reactivity pilot fuel (e.g. diesel or DME) to ignite a low-reactivity lean premixed charge (e.g. methane/air), have been proposed to meet stringent pollutant regulations. Due to the complex multiscale interaction among flow, chemistry and flames, DF combustion exhibits a complicated, multi-modal combustion regimes and is hence challenging to model. Ignition delay time (IDT), as one of the most important parameters, is typically considered to develop an understanding and modeling strategy for complex ignition processes. However, accurate calculations and measurements of the IDTs over a wide range of fuel blends, pressures and flow conditions is a time-consuming, complicated procedure. While several physics-based IDT models have been proposed for single fuel ignition, they are subject to some limitations in DF scenarios. In this work, two different supervised Machine Learning methods: a glass box - High Dimensional Model Representation (HDMR) and a black box - Convolutional Neutral Network (CNN) are employed to seek an accurate and efficient prediction of the IDTs of DF. First, the underlying mechanisms of DF interaction during the ignition process are investigated. The results show that the DF ignition process is highly complex, involving negative-temperature coefficient (NTC) behavior, two-stage ignition, and multiple combustion modes and transition. Then, data needed to train HDMR and CNN is generated by a large number of transient counterflow and homogeneous reactor calculations covering DF engine conditions. The trained HDMR and CNN models are tested with numerical and experimental databases. The results show that both HDMR and CNN can capture the features of DF ignition and correctly predict IDTs, even for predictions outside the ranges of parameters used for learning. Compared to CNN, HDMR is more favorable due to its relatively weak dependence on the size of training data and its ability to assess the sensitivity of IDTs to input variables. The sensitivity analysis suggests that the mixing rate between the pilot fuel and the main fuel plays a critical role in affecting the DF ignition. The HDMR and/or CNN models are seen as promising alternatives to time-consuming experimental measurements or numerical calculations of IDTs.

Automated summary

Dual-fuel compression ignition engines use a high-reactivity pilot fuel to ignite a low-reactivity lean premixed charge, resulting in complex combustion regimes that are challenging to model. Supervised Machine Learning methods like HDMR and CNN can accurately predict IDTs, with HDMR showing more advantages over CNN in terms of dependence on training data size.