Inverted phi (φ)-sensitivity is a new approach of NOx reduction in compression-ignition (C.I.) engines. Previously, pure ethanol (E100) was selected as the preliminary test fuel in a single injection compression-ignition engine, and was shown to have good potential for low engine-out NOx emissions under low and medium load conditions due to its inverted ignition sequence. Under high load, however, the near-stoichiometric and non-homogeneous fuel/air distribution removes the effectiveness of the inverted φ-sensitivity. Therefore, it is desirable to recover the combustion sequence in the chamber such that the leaner region is burned before the near-stoichiometric region. When the combustion in near-stoichiometric region is inhibited, the temperature rise of that region is hindered and the formation of NOx is suppressed. To achieve the goal of homogenizing the mixture before combustion, thus switching ignition mode and lowering emissions when fueling with the target fuel, multiple direct-injection strategies are applied to this study. 3-D engine CFD simulations are conducted with different multiple injection strategies under high-load operations in a compression-ignition engine. The injection characteristics of optimized cases are examined in KIAV-3V coupled with a Genetic Algorithm(GA). An objective function is used to qualify the realization of optimized cases with minimized engine-out NOx, carbon monoxide (CO), soot and unburned hydrocarbon (UHC), while preserving engine performance. It is found that with multiply direct-injection strategies, the desired inverted φ-sensitivity dominated ignition can be regained under high-load engine operation conditions, uniform fuel-air mixture before combustion can be retrieved, and lower in-cylinder temperature and pressure are possible. Comparing to the double-injection strategy, the optimized triple-injection strategy shows more pronounced effects in terms of combustion quality and emission reduction.
ASJC Scopus subject areas
- Automotive Engineering
- Safety, Risk, Reliability and Quality
- Industrial and Manufacturing Engineering