Experimental Comparison of Different Cycle-Based Methodologies for the INDICATING in Hydrogen-Fueled Internal Combustion Engines

Abstract

<div class="section abstract"><div class="htmlview paragraph">High cycle-to-cycle variations (CTCV) in a Hydrogen-Fueled Internal Combustion Engine (H2-ICE), especially in the lean-burn condition, not only lower the engine’s efficiency but also increase emissions and torque variations. High CTCV are mainly due to the variations in: mixture motion within the cylinder at the time of spark, amount of air and fuel fed to the cylinder, and mixing of the fresh mixture and residual gases within the cylinder during each cycle. In this article, multiple cycle-based methodologies were compared and analyzed specifically for H2-ICEs based on systematic experimentation. The experimental test campaign was performed on a Port Fuel Injection (PFI) H2-ICE designed by PUNCH Torino and data is processed with MATLAB. A MATLAB code is also proposed as a tool for comparing multiple methodologies for the analysis of CTCV specifically for H2-ICE. In order to compare different methodologies, the operating conditions of the H2-ICE were kept constant for all the results except the Matekunas plot. In this study, various pressure-related parameters such as Peak Firing Pressure (PFP), maximum rate of pressure rise, crank angle at which maximum pressure occurs, crank angle at which maximum rate of pressure occurs, and Indicated Mean Effective Pressure (IMEP), as well as burn-rate related parameters like apparent heat release, heat loss to the walls, total heat release, mass burning rate were plotted and interpreted. Matekunas plots have the potential to depict cyclic variations as these plots show the variations of magnitude in <i>p</i>_max and <i>θ</i>_pmax for each cycle in terms of varying the burning rate and crank angle at which combustion effectively starts (e.g., <i>θ</i> at MFB1). This study aims to provide engineers and researchers practical insights to improve the performance of H2-ICE through methodologies for accurate INDICATING analysis, by analyzing cyclic variations and providing tools for their understanding.</div></div>