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The pre-chamber combustion can extend the lean limit of internal combustion engines (ICE) and hence increase their overall efficiency. Compared to active pre-chambers equipped with an auxiliary fuel supply system, passive pre-chambers have lower manufacturing costs and require minimal or no design modifications to the conventional spark plug engines. The major challenge of the passive pre-chamber is to extend the lean limit as much as the active pre-chamber. Computational fluid dynamics (CFD) simulations were conducted on a light-duty single-cylinder engine geometry fitted with a passive pre-chamber and using iso-octane as fuel to investigate and optimize the passive pre-chamber fuel enrichment through the pre-chamber nozzles. The non-reacting flow simulations were performed from the intake valve open (IVO) to spark timing.

In modern compression ignition engines, the dense liquid fuel is directly injected into high pressure and temperature atmosphere, so the spray transitions from subcritical to supercritical conditions. To gain better control of the spray-combustion heat release process, it is important to have a physically accurate description of the spray development process. This work explored the effect of real-fluid thermodynamics in the computational prediction of multiphase flow for two non-ideal situations: the cryogenic nitrogen and non-cryogenic n-dodecane and ammonia sprays. Three real-fluid equations of state (EoS) such as the Soave-Redlich-Kwong (SRK), Peng-Robinson (PR), and Redlich-Kwong-Peng-Robinson (RKPR) coupled with the real-fluid Chung transport model were implemented in OpenFoam to predict the real-fluid thermodynamic properties. Validations against the CoolProp database were conducted.

Flame lift-off length (FLOL), ignition delay time (IDT), liquid length (LL), and Soot are essential parameters defining spray combustion characteristics. They help understand the combustion dynamics and validate the spray and combustion models for numerical simulations. However, obtaining extensive data from experiments is costlier and time-consuming. Machine learning (ML) models have advanced to the point where they could create efficient models that could be used as surrogates for experiments. In this study, five different ML algorithms have been trained using the experimental dataset available through the engine combustion network (ECN) community. A novel genetic algorithm-based hyperparameter optimization code has been used to optimize the models to improve prediction accuracy. The model performances were compared, and the better model was chosen as an experimental surrogate to predict FLOL, IDT, LL, and Soot.

Gasoline compression ignition (GCI) engines are becoming more popular alternative for conventional spark engines to harvest the advantage of high volatility. Recent experimental study demonstrated that high reactivity gasoline fuel can be operated in a conventional mixing controlled combustion mode producing lower soot emissions than that of diesel fuel under similar efficiency and NOx level [1]. Therefore, there is much interest in using gasoline-like fuels in compression ignition engines. In order to improve the fidelity of simulation-based GCI combustion system development, it is mandatory to enhance the prediction of spray combustion of gasoline-like fuels. The purpose of this study is to model the spray characteristics of high reactivity gasoline fuels and validate the models with experimental results obtained through an optically accessible constant volume vessel under vaporizing [2] and reactive conditions [3].