The benefits of pre-chamber combustion (PCC), such as improved engine efficiency and reduced NOx emissions, are primarily observed when operating at lean conditions with an active pre-chamber, where auxiliary fuel is supplied directly to the pre-chamber. Estimating the pre-chamber excess air ratio (λ) is important in the active pre-chamber concept to gain insights into the pre-chamber combustion phenomenon. Experimental investigations were performed using a narrow-throat pre-chamber at global-λ 1.6, 1.8, and 2.0. The fraction of fuel energy injected in the pre-chamber over the total fuel energy was fixed at 3%, 7%, and 13% for each global-λ. The mixture formation process inside the pre-chamber is first simulated using the 1-D simulation software GT-Power to analyze the pre-chamber λ at the ignition timing. However, the 1-D results were unable to reproduce the experimental observations on the pre-chamber pressure buildup accurately. Upon simulating the same conditions using the 3-D CFD software CONVERGE, the pre-chamber λ estimated from the CFD model is well-correlated to the experimental data. The CFD results indicate that the amount of fuel trapped in the pre-chamber at the inlet valve closing timing is over-predicted by the 1-D simulations. A correlation between the injected and the trapped fuel in the pre-chamber is proposed by theoretical scavenging models and applied to the 1-D simulation results to improve pre-chamber λ prediction accuracy.

An optimized active pre-chamber geometry was obtained by combining computational fluid dynamics (CFD) and machine learning (ML). A heavy-duty engine operating with methane under lean conditions was considered. The combustion process was modeled with a multi-zone well-stirred reactor (MZ-WSR) with a skeletal methane oxidation mechanism. The simulations were run for a complete cycle. For the optimization study, the pre-chamber was parametrized; six independent and three dependent variables were considered, while the volume was kept constant. Three hundred pre-chamber designs were generated, and a one-shot design of experiments (DoE) optimization was first considered. A merit function was adopted to rank the designs, and an optimum design was found from the DoE results, which yielded considerable improvements in merit ranking, considering fuel consumption, engine-out emissions, noise, and safety; secondly, machine learning algorithms were trained by utilizing the DoE results aiming at finding a globally optimum geometry for the considered operating condition. Five sequential iterations were performed, and the ML algorithms were capable of proposing a new design with superior performance compared to the best DoE.

Typical automotive emission testing systems usually employ Flame Ionization Detection (FID) analyzers to measure unburned fuel species in the exhaust, but the technique is not suitable for engines operating on alcohol fuels. The FID method is not sensitive to measuring unburned alcohol fuels due to the presence of oxygen bonds in the fuel molecule. Other techniques, such as Fourier Transform Infrared (FTIR), can provide accurate unburned fuel measurements with alcohol fuel. However, these techniques are expensive and are less accessible compared to FID analyzers. In this study, the unburned fuel emissions from the engine exhaust were measured simultaneously with FID and FTIR analyzers, with the engine operating on pure alcohols, which are methanol, ethanol, and n-butanol. While most previous work focuses on stoichiometric air-fuel mixtures, a wide range of lean operating conditions between global-λ 1.6 to 2.8 will be tested in this study. Such ultra-lean operation was achievable thanks to the narrow-throat pre-chamber combustion concept with the pre-chamber fueled with methane. The unburned fuel measurements from the two analyzers are compared, and correction factors for each fuel are identified to rectify the FID unburned fuel measurements based on the results provided by the FTIR.