Injection rate measurements and Machine-Learning based predictions of ECN Spray A-3 piezoelectric injector

Abstract

This study investigates the impact of parametric conditions on the rate of injection (ROI) and injection quantities of a new piezo-type Engine Combustion Network (ECN) Spray A-3 injector using a combination of experimental and model-based approaches. The experiments were conducted using a Zeuch-based HDA Moehwald injection rate measurement system to obtain ROI data. An artificial neural network (ANN) algorithm was employed to develop a predictive model for ROI data, accurately capturing injection behaviors and ROI patterns under a wide range of conditions. The temporal ROI data were trained using a Bayesian regularization backpropagation model with four input conditions: chamber temperature, chamber pressure, injection pressure, and injection duration. This study examined the influence of these conditions on the transient profiles of the ROI, quasi-steady ROI, injection duration, and total injection mass for both the experimental and predicted results. The model demonstrated good predictions that aligned well with the experimental measurements, indicating a significant increase in the ROI and injection quantities with an increase in injection pressure, while the effects of chamber temperature and pressure were less pronounced. The ANN model used in this study allows for the prediction of various ROI profiles without the need for additional experiments or computational fluid dynamics techniques, and complex physical modeling, thereby substantially reducing the cost and time involved in developing injection control systems for advanced internal combustion engines.