Numerical Study on the Effect of Fuel Rich n-Heptane on In-Cylinder Fuel Reforming Characteristics in an HCCI Engine

The effect of in-cylinder fuel reforming on an n-heptane homogenous charge compression ignition engine has been studied. A dedicated cylinder without a complex control system is proposed for fuel enrichment reforming, which can provide part of the power for the engine. The effects of different reforming species on engine performance and chemical reaction have been simulated by a numerical study. By comparing the combustion characteristics of n-heptane with different equivalence ratios in the reformer cylinder, the optimal n-heptane equivalence ratio has been determined. The enrichment of n-heptane produces sufficient hydrogen (H2) and carbon monoxide (CO), while the hydrocarbon content of the reforming species was low. It was found that the addition of reforming species retards the combustion phase of n-heptane, thereby providing a means of controlling engine performance. In addition, the laminar flame speed and the adiabatic flame temperature of n-heptane increased by adding H2 and CO. Fuel reforming reduced the emission of ethylene, propyne, allene, propylene, butadiene, and nitrogen oxide, but it increased the emissions of acetylene and CO. Moreover, chemical, dilution, and thermodynamic effects of the reforming gas have been studied. The results showed that the chemical effect of the reforming species was less significant than the dilution and thermodynamic effects. These simulation results showed that in-cylinder fuel reforming can effectively improve engine performance and thereby reduce emissions.

Prediction of Reformed Gas Composition for Diesel Engines with a Reformed EGR System Using an Artificial Neural Network

Facing the reinforced emission regulations and moving toward a clean powertrain, hydrogen has become one of the alternative fuels for the internal combustion engine. In this study, the prediction methodology of hydrogen yield by on-board fuel reforming under a diesel engine is introduced. An engine dynamometer test was performed, resulting in reduced particulate matter (PM) and NOx emission with an on-board reformer. Based on test results, the reformed gas production rate from the on-board reformer was trained and predicted using an artificial neural network with a backpropagation process at various operating conditions. Additional test points were used to verify predicted results, and sensitivity analysis was performed to obtain dominant parameters. As a result, the temperature at the reformer outlet and oxygen concentration is the most dominant parameters to predict reformed gas owing to auto-thermal reforming driven by partial oxidation reforming process, dominantly.