Numerical optimization of methane-based fuel blends under engine-relevant conditions using a multi-objective genetic algorithm

The objective of this work is to examine in a systematic way, how conflicting requirements such as maximumignition delay time and laminar flame speed can be met by adding gaseous components to methane in order toobtain the optimal fuel blend under engine-relevant conditions. Low-dimensional models are coupled with amulti-objective optimization algorithm in order to compute optimal methane/hydrogen, methane/syngas andmethane/propane/syngas blend compositions that maximize simultaneously the ignition delay time, the laminarflame speed and the Wobbe number. The non-dominated sorting genetic algorithm (NSGA-II) is used to generatea set of Pareto solutions, and the best compromise solutions are then determined by the technique for orderpreference by similarity to ideal solution (TOPSIS).It was found that the GRI-Mech 3.0 mechanism could notaccurately predict ignition properties of methane-based fuel blends under engine-relevant conditions. The op-timization results revealed that initial conditions have a significant effect on the optimal fuel blend composition.For methane/hydrogen and methane/syngas blends, pure methane was the optimal fuel at high temperaturesand low equivalence ratios, while high hydrogen contents were beneficial at lower temperatures. When theignition delay time is of higher importance, the optimal composition shifted towards higher carbon monoxidecontents. Blends with higher hydrogen and syngas contents resulted in reduced ignition delay times and higherlaminar flame speeds. Regarding the methane/propane/syngas blend, the presence of propane in the optimalblend was found to be more favorable than hydrogen and carbon monoxide to satisfy the objectives