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Machine learning models were shown to provide faster results but with similar accuracy to multidimensional computational fluid dynamics or in-depth experiments. This study used a support-vector machine (SVM), a set of related supervised learning methods, to predict the dynamic performance (i.e., engine power and torque) of a heavy-duty natural gas spark ignition engine. The single-cylinder four-stroke test engine was fueled with methane. The engine was operated at different spark timings, mixture equivalence ratios, and engine speeds to provide the data for training and testing the proposed SVM. The results indicated that the performance and accuracy of the built regression model were satisfactory, with correlation coefficient quantities all larger than 0.95 and root-mean-square errors close to zero for both training and validation datasets.

3D CFD spark-ignition IC engine simulations are extremely complex for the regular user. Truly-predictive CFD simulations for the turbulent flame combustion that solve fully coupled transport/chemistry equations may require large computational capabilities unavailable to regular CFD users. A solution is to use a simpler phenomenological model such as the G-equation that decouples transport/chemistry result. Such simulation can still provide acceptable and faster results at the expense of predictive capabilities. While the G-equation is well understood within the experienced modeling community, the goal of this paper is to document some of them for a novice or less experienced CFD user who may not be aware that phenomenological models of turbulent flame combustion usually require heavy tuning and calibration from the user to mimic experimental observations.

Ammonia is a zero-carbon candidate fuel for the decarbonization of internal combustion (IC) engines. A concern when using ammonia in IC engines is the increased emissions of nitrogen oxides (NOX), due to the additional nitrogen in the ammonia molecule. Compared to conventional petroleum such as gasoline and diesel, ammonia combustion adds the fuel NOX formation mechanism in addition to the original thermal NOX generation pathway, which further complicates the NOX emission characteristics of ammonia engines. Decoupling fuel NOX and thermal NOX helps to increase the understanding of the formation and evolutionary characteristics of nitrogen oxides occurring inside ammonia engines, but the available literature lacks studies in this respect. The purpose of this study is to fill this research gap and to propose a methodology for decoupling fuel NOX and thermal NOX.