Optimization of a compression -ignition engine fueled with diesel and gasoline -like fuels

Shi, Y. Optimization of a Compression -Ignition Engine Fueled With Diesel and Gasoline -Like Fuels. University of Wisconsin-Madison, 2009.

Three different multi-objective genetic algorithms, including the Micro-genetic algorithm (?-GA), Non-dominated Sorting Genetic Algorithm II (NSGA II), and Adaptive Range Multi-Objective Genetic Algorithm (ARMOGA), were assessed with respect to their performance of optimality and diversity of results for engine optimization. NSGA II was found to perform the best for the optimization problems of interest.

The high computational expense of the chemistry solver with detailed chemistry in engine CFD tools can be prohibitive for engine optimization problems. The present work proposed two methodologies to accelerate the chemistry solver. The first method, the Adaptive Multi-grid Chemistry (AMC) model, was developed to group thermodynamically-similar cells in order to reduce the calling frequency to the chemistry solver. The second method, the Extended Dynamic Adaptive Chemistry (EDAC) scheme, was proposed to reduce the size of the chemical mechanism on-the-fly, based on the local and instantaneous thermal conditions. By combining these two methods, more than an order of magnitude acceleration was achieved for Homogeneous Charge Compression Ignition (HCCI) simulations and more than four-fold speed-up was obtained for Direct-Injection (DI) engine simulations. The simulation results were highly consistent with the full chemistry solver.

The computational tools were then applied to engine optimization. The piston profile and injection strategies were optimized for a heavy-duty diesel engine operated under low- and high-load using the NSGA II and the improved KIVA3v2 code with simplified combustion models. The results indicated that by choosing an optimal combustion chamber design from the high-load optimization study and varying swirl ratio, injection timing and pressure, excellently performing designs were also found using the high-load optimal chamber geometry for the low-load condition.

Finally, the KIVA code with the efficient chemistry solvers was used in an optimization study of a heavy-duty CI engine fueled with diesel and gasoline-like fuels and operated under mid- and high-load conditions. The results demonstrated a significant dependency of the optimal injection strategies on the physical and chemical properties of fuels. This study also indicates that CI engines fueled with gasoline-like fuels show great potential to meet more stringent emission regulations, as well as to improve fuel economy compared to conventional diesel.