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Biodiesel-fueled engine simulations were performed using the KIVA3v-Release 2 code coupled with Chemkin-II for detailed chemistry. The model incorporates a reduced mechanism that was created from a methyl decanoate/methyl-9-decenoate mechanism developed at the Lawrence Livermore National Laboratory. A combination of Directed Relation Graph, chemical lumping, and limited reaction rate tuning was used to reduce the detailed mechanism from 3299 species and 10806 reactions to 77 species and 209 reactions. The mechanism was validated against its detailed counterpart and predicted accurate ignition delay times over a range of relevant operating conditions. The mechanism was then combined with the ERC PRF mechanism to include n-heptane as an additional fuel component. The biodiesel mechanism was applied in KIVA using a discrete multi-component model with accurate physical properties for the five common components of real biodiesel fuel.

The present work proposes a methodology for diesel engine development using scaling laws and computational optimization with multi-dimensional CFD tools. A previously optimized 450cc HSDI diesel engine was down-scaled to 400cc size using recently developed scaling laws. The scaling laws were validated by comparing the performance of these two engines, including pressure, HRR, peak and averaged temperature, and pollutant emissions. A novel optimization methodology, which is able to simultaneously optimize multiple operating conditions, was proposed. The method is based on multi-objective genetic algorithms, and was coupled with the KIVA3V Release 2 code to further optimize the down-scaled diesel engine. An adaptive multi-grid chemistry model was used in the KIVA3V code to reduce the computational cost of the optimization. The computations were conducted using high-throughput computing with the CONDOR system.

The ignition process of fuel reactivity controlled PCCI combustion was investigated using engine experiments and detailed CFD modeling. The experiments were performed using a modified all metal heavy-duty, compression-ignition engine. The engine was fueled using commercially available gasoline (PON 91.6) and ULSD diesel delivered through separate port and direct injection systems, respectively. Experiments were conducted at a steady state-engine load of 4.5 bar IMEP and speed of 1300 rev/min. In-cylinder optical measurements focused on understanding the fuel decomposition and fuel reactivity stratification provided through the charge preparation. The measurement technique utilized point location optical access through a modified cylinder head with two access points in the firedeck. Optical measurements of natural thermal emission were performed with an FTIR operating in the 2-4.5 μm spectral region.

This study explores an Adaptive Injection Strategy (AIS) that employs multiple injections at both low and high pressures to reduce spray-wall impingement, control combustion phasing, and limit pressure rise rates in a Premixed Compression Ignition (PCI) engine. Previous computational studies have shown that reducing the injection pressure of early injections can prevent spray-wall impingement caused by long liquid penetration lengths. This research focuses on understanding the performance and emissions benefits of low and high pressure split injections through experimental parametric sweeps of a 0.48 L single-cylinder test engine operating at 2000 rev/min and 5.5 bar nominal IMEP. This study examines the effects of 2nd injection pressure, EGR, swirl ratio, and 1st and 2nd injection timing, for both single heat release and two-peak high temperature heat release cases. In order to investigate the AIS concept experimentally, a Variable Injection Pressure (VIP) system was developed.

The present work proposes a methodology for diesel engine development using multi-dimensional CFD and computer optimization. A multi-objective genetic algorithm coupled with the KIVA3V Release 2 code was used to optimize a high speed direct injection (HSDI) diesel engine for passenger car applications. The simulations were conducted using high-throughput computing with the CONDOR system. An automated grid generator was used for efficient mesh generation with 11 variable piston bowl geometry parameters. The first step in the procedure was to search for an optimal nozzle and piston bowl design. In this case, spray targeting, swirl ratio, and piston bowl shape were optimized separately for two full-load cases using simpler efficient combustion models (the characteristic time scale model and the shell ignition model). The optimal designs from the two optimizations were then validated using a combustion model with detailed chemistry (KIVA-CHEMKIN model and ERC n-heptane mechanism).