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Accelerated computational optimization of a diesel engine calibration was achieved by combining Support Vector Regression models with the Particle Swarm Optimization routine. The framework utilized a full engine simulation as a surrogate for a real engine test with test parameters closely resembling a typical 4.5L diesel engine. Initial tests were run with multi-modal test problems including Rastragin's, Bukin's, Ackely's, and Schubert's functions which informed the ML model tuning hyper-parameters. To improve the performance of the engine the hybrid approach was used to optimize the Fuel Pressure, Injection Timing, Pilot Timing and Fraction, and EGR rate. Nitrogen Oxides, Particulate Matter, and Specific Fuel Consumption are simultaneously reduced. As expected, optimums reflect a late injection strategy with moderately high EGR rates.

In this study both double and triple injection strategies were used with fuel pressures up to 300 and 250 MPa, respectively. Tests were conducted at medium load conditions with cooled, high-pressure EGR at a ratio of 40% and higher. A four-cylinder production engine, featuring double turbochargers with one variable geometry turbocharger, was tested. The double injection strategy consisted of a 20% close-coupled pilot injection while the triple injection strategy introduced a post injection consisting of 10% the total cycle fuel. Results of this study do not indicate an advantage to extreme fuel pressure. The increased air entrainment reduces soot while increasing the premixed burn heat release, mean cylinder temperature, and NOx. Compared to the double injection scheme, triple injections achieved much lower soot for the same EGR rate with only a small NOx penalty.

The objective of this work was to identify methods of reliably predicting optimum operating conditions in an experimental compression ignition engine using multiple injections. Abstract modeling offered an efficient way to predict large volumes data, when compared with simulation, although the initial cost of constructing such models can be large. This work aims to reduce that initial cost by adding knowledge about the favorable network structures and training rules which are discovered. The data were gathered from a high pressure common rail direct injection turbocharged compression ignition engine utilizing a high EGR configuration. The range of design parameters were relatively large; 100 MPa - 240 MPa for fuel pressure, up to 62% EGR using a modified, long-route, low pressure EGR system, while the pilot timing, main timing, and pilot ratio were free within the safe operating window for the engine.

Particle Swarm and the Genetic Algorithm were coupled to optimize multiple performance metrics for the combustion of neat biodiesel in a turbocharged, four cylinder, John Deere engine operating under constant partial load. The enhanced algorithm was used with five inputs including EGR, injection pressure, and the timing/distribution of fuel between a pilot and main injection. A merit function was defined and used to minimize five output parameters including CO, NOx, PM, HC and fuel consumption simultaneously. The combination of PSO and GA yielded convergence to a Pareto regime without the need for excessive engine runs. Results along the Pareto front illustrate the tradeoff between NOx and particulate matter seen in the literature.

Accurate modeling of evaporating sprays is critical for diesel engine simulations. The standard spray and evaporation models in KIVA-3V tend to under-predict the vapor penetration, especially at high ambient pressure conditions. A sharp decrease of vapor penetration gradient is observed soon after the liquid spray is completely evaporated due to the lack of momentum sources beyond the liquid spray region. In this study, a gas particle model is implemented in KIVA-3V which tracks the momentum sources resulting from the evaporated spray. Lagrangian tracking of imaginary gas particles is considered until the velocity of the gas particle is comparable to that of the gas phase velocity. The gas particle continuously exchanges momentum with the gas phase and as a result the vapor penetrations are improved. The results using the present gas particle model is compared with experimental data over a wide range of ambient conditions and good levels of agreement are observed in vapor penetration.

The simultaneous reduction of particulate matter (PM) and nitrous oxides (NOx) emissions form diesel exhaust is key to current research activities. Although various technologies have been introduced to reduce emissions from diesel engines, the in-cylinder reduction of PM and NOx due to improved combustion mechanisms will continue to be an important field in research and development of modern diesel engines. Furthermore increasing prices and question over the availability of diesel fuel derived from crude oil has introduced a growing interest. Hence it is most likely that future diesel engines will be operated on pure biodiesel and/or blends of biodiesel and crude oil-based diesel. In this study the performance of different biodiesel blends under low temperature combustion conditions (i.e., high exhaust gas recirculation and advanced fuel injection schemes) was investigated.