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Past research has shown that multidimensional computational fluid dynamics modeling in combination with a genetic algorithm method is an effective approach for optimizing internal combustion engine design. However, optimization studies performed with a detailed computational fluid dynamics model are time intensive, which limits the practical application of this approach. This study addresses this issue by using a machine learning approach called Gaussian process regression in combination with computational fluid dynamics modeling to reduce the computational optimization time. An approach was proposed where the Gaussian process regression model could be used instead of the computational fluid dynamics model to predict the outputs of the genetic algorithm optimization. In this approach, for every nth generation of the genetic algorithm, the data from the previous n − 1 generations was used to train the Gaussian process regression model. The approach was tested on an engine optimization study with five input parameters. When the genetic algorithm was run solely with computational fluid dynamics, the optimization took 50 days to complete. In comparison with the computational fluid dynamics and Gaussian process regression approach, the computational time was reduced by 62%, and the optimization was completed in 19 days using the same amount of computational resources. Additional parametric studies were performed to investigate the impact of genetic algorithm + Gaussian process regression parameters. Results showed that either reducing the initial dataset size or relaxing the error criterion resulted in increased Gaussian process regression evaluations within the genetic algorithm. However, relaxing the error criterion was found to impact the model predictions negatively. The initial dataset size was found to have a negligible impact on the final optimum design. Finally, the potential of machine learning in further improving the optimization process was explored by using the Gaussian process regression model to check for the robustness of the designs to operating parameter variations during the optimization. The genetic algorithm was repeated with the modified procedure and it was shown that adding the stability check resulted in a different, more reliable and stable optimum solution.

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Computational optimizations of dual-fuel reactivity controlled compression ignition combustion and gasoline compression ignition combustion were performed using a novel adaptive dual-fuel injector capable of direct injecting both gasoline and diesel fuel in a single cycle. Optimization used the Engine Research Center KIVA code coupled with a multiobjective genetic algorithm. Model validation was performed by comparing simulation results to conventional diesel, reactivity controlled compression ignition, and gasoline compression ignition combustion, and the validated model was used to develop an optimum reactivity controlled compression ignition–gasoline compression ignition combustion strategy. The reactivity controlled compression ignition optimization results showed that by direct injecting gasoline and diesel fuel, the gasoline quantity can be held at a high percentage across the range of loads considered. In this study, the mode weighted gasoline percentage was 91%. At the lightest load point, direct injecting the gasoline gave optimum results, whereas for the other load points, premixing the gasoline yielded the optimum results. The optimized results were compared with conventional diesel combustion, and it was seen that reactivity controlled compression ignition combustion gives a cycle-averaged improvement of 33% in gross indicated efficiency over conventional diesel combustion. The cycle-averaged NOx and soot emissions were reduced by 95% and 75%, respectively. To demonstrate operation over the entire operating map, an optimization was performed at a high-speed–high-load (16 bar, 2500 r/min) condition. Optimization results showed that a gross indicated efficiency of 46.4% with near zero NOx and soot emissions could be achieved using gasoline compression ignition at this load point.

Low temperature, highly premixed compression ignition strategies have proven to produce high efficiency and low soot emissions, but struggle to reach high loads within normal operating constraints. Recent research has suggested that a mixed mode combustion strategy using a premixed main heat release followed by a mixing controlled load extension injection can retain the part-load thermal efficiency and emissions reduction potential of premixed compression ignition strategies, while enabling high load operation. However, soot emissions under this type of mixed mode combustion strategy have been shown to be problematic. This work investigates soot formation and mitigation methods using a combination of detailed engine experiments and computational fluid dynamics modeling. A premixed compression ignition combustion event was achieved using a premixed charge of gasoline and n-heptane to control combustion phasing, and a load extension injection of gasoline was added near top dead center. The experiments showed negligible engine out soot under the premixed compression ignition operating conditions (i.e. without the load extension injection). When the load extension injection was added, soot increased by several orders of magnitude. Detailed experiments were used to isolate the effects of injection timing, injection pressure, charge conditions (e.g. air-fuel ratio), and fuel type. Computational fluid dynamics modeling considering polycyclic aromatic hydrocarbon chemistry up to pyrene was then used to explain the experimentally observed soot trends. As expected, the soot emission results showed a strong impact of oxygen concentration and injection pressure for injection timings near top dead center; however, as the load extension injection event was delayed beyond the end of the premixed compression ignition heat release, the soot formation decreased and became independent of oxygen concentration. At these conditions, the computational fluid dynamics modeling showed that soot formation is dependent solely on temperature. The results identify a pathway to enable premixed compression ignition load extension, while minimizing soot emissions.