Acceleration of Modeling Capability for GDI Spray by Machine-Learning Algorithms

Abstract

Cold start causes a high amount of unburned hydrocarbon and particulate matter emissions in gasoline direct injection (GDI) engines. Therefore, it is necessary to understand the dynamics of spray during a cold start and develop a predictive model to form a better air-fuel mixture in the combustion chamber. In this study, an Artificial Neural Network (ANN) was designed to predict quantitative 3D liquid volume fraction, liquid penetration, and liquid width under different operating conditions. The model was trained with data derived from high-speed and Schlieren imaging experiments with a gasoline surrogate fuel, conducted in a constant volume spray vessel. A coolant circulator was used to simulate the low-temperature conditions (−7 °C) typical of cold starts. The results showed good agreement between machine learning predictions and experimental data, with an overall accuracy R2 of 0.99 for predicting liquid penetration and liquid width. In addition, the developed ANN model was able to predict detailed dynamics of spray plumes. This confirms the robustness of the ANN in predicting spray characteristics and offers a promising tool to enhance GDI engine technologies.