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The objective of this investigation is to optimize light-duty diesel engine operating parameters using Adaptive Injection Strategies (AIS) for optimal fuel preparation. A multi-dimensional Computational Fluid Dynamics (CFD) code with detailed chemistry, the KIVA-CHEMKIN code, is employed and a Multi-Objective Genetic Algorithm (MOGA) is used to study a Two-Stage Combustion (TSC) concept. The combustion process is considered at a light load operating condition (nominal IMEP of 5.5 bar and high speed (2000 rev/min)), and two combustion modes are combined in this concept. The first stage is ideally Homogeneous Charge Compression Ignition (HCCI) combustion and the second stage is diffusion combustion under high temperature and low oxygen concentration conditions. Available experimental data on a 1.9L single-cylinder research engine is used for model validation.

A three-dimensional homogeneous equilibrium model (HEM) has been developed and implemented into an engine computational fluid dynamics (CFD) code KIVA-3V. The model was applied to simulate cavitating flow within injector nozzle passages. The effects of nozzle passage geometry and injection conditions on the development of cavitation zones and the nozzle discharge coefficient were investigated. Specifically, the effects of nozzle length (L/D ratio), nozzle inlet radius (R/D ratio) and K or KS factor (nozzle passage convergence) were simulated, and the effects of injection and chamber pressures, and time-varying injection pressure were also investigated. These effects are well captured by the nozzle flow model, and the predicted trends are consistent with those from experimental observations and theoretical analyses.

A micro-genetic algorithm (μGA) optimization method was applied to a heavy-duty, direct-injected diesel engine via an automated test bed system. The goal of this application was to demonstrate the feasibility and advantages of automated optimization experiments. With the genetic algorithm, no user input was required other than the factors of interest and their allowable ranges. This means that once the routine was initiated, it was essentially run undisturbed until a preset objective level was reached or a preset number of generations had been run. The automated μGA was successfully demonstrated at all points of the six-mode transient cycle simulation, excluding idle. To accomplish the automated experiments, an automated testing system was developed around a Caterpillar single-cylinder diesel engine.

Optimizations were performed on a single-cylinder heavy-duty Caterpillar SCOTE 3401E engine for NOx, PM and BSFC reductions. The engine was equipped with a Caterpillar 300B HEUI fuel injection system capable of up to four injections with timings from 90 BTDC to 90 ATDC. The engine was operated at a medium load (57%), high speed (1737 rev/min) operation point. A micro-genetic algorithm was utilized to optimize a hybrid, double-injection strategy, which incorporated an early, premixed pilot injection with a late main injection. The fuel injection parameters, intake boost pressure, and EGR were considered in the optimization. The optimization produced a parameter set that met the 2007 and 2010 PM emissions mandate of 0.0134 g/kW-hr, and was within the 1.5x not to exceed NOx + HC mandate of 2.694 g/kW-hr. Following the optimization exercise, further parametric interaction studies were performed to reveal the underlying interactions and phenomena.

Low-temperature combustion concepts that utilize cooled EGR, early/retarded injection, high swirl ratios, and modest compression ratios have recently received considerable attention. To understand the combustion and, in particular, the soot formation process under these operating conditions, a modeling study was carried out using the KIVA-3V code with an improved phenomenological soot model. This multi-step soot model includes particle inception, surface growth, surface oxidation, and particle coagulation. Additional models include a piston-ring crevice model, the KH/RT spray breakup model, a droplet wall impingement model, a wall heat transfer model, and the RNG k-ε turbulence model. The Shell model was used to simulate the ignition process, and a laminar-and-turbulent characteristic time combustion model was used for the post-ignition combustion process.

Experiments were performed on a single-cylinder heavy-duty Caterpillar SCOTE 3401E engine at high speed (1737 rev/min) and loads up to 60% of full load for fully Premixed Charge Compression Ignition (PCCI) combustion. The engine was equipped with a high pressure (150 MPa) Caterpillar 300B HEUI fuel injection system. The engine was run with EGR levels up to 75% and with equivalence ratios up to 0.95. These experiments resulted in compliance of NOx and PM emissions to 2010 emissions mandates levels up to the tested load. The set of experiments also demonstrated the importance of cylinder charge preparation by way of optimized start-of-combustion timing for sufficient in-cylinder mixing. It was found that increased EGR rates, even with the correspondingly increased equivalence ratios, increase mixing time and substantially decrease PM emissions.