The standard capability of engine experimental studies is that ensemble averaged quantities like in-cylinder pressure from multiple cycles and emissions are reported and the cycle to cycle variation (CCV) of indicated mean effective pressure (IMEP) is captured from many consecutive combustion cycles for each test condition. However, obtaining 3D spatial distribution of all the relevant quantities such as fuel-air mixing, temperature, turbulence levels and emissions from such experiments is a challenging task. Computational Fluid Dynamics (CFD) simulations of engine flow and combustion can be used effectively to visualize such 3D spatial distributions. A dual fuel engine is considered in the current study, with manifold injected natural gas (NG) and direct injected diesel pilot for ignition. Multiple engine cycles in 3D are simulated in series like in the experiments to investigate the potential of high fidelity RANS simulations coupled with detailed chemistry, to accurately predict the CCV. Cycle to cycle variation (CCV) is expected to be due to variabilities in operating and boundary conditions, in-cylinder stratification of diesel and natural gas fuels, variation in in-cylinder turbulence levels and velocity flow-fields. In a previous publication by the authors , variabilities in operating and boundary conditions are incorporated into several closed cycle simulations performed in parallel. Stochastic variations/stratifications of fuel-air mixture, turbulence levels, temperature and internal combustion residuals cannot be considered in such closed cycle simulations. In this study, open cycle simulations with port injection of natural gas predicted the combined effect of the stratifications on the CCV of in-cylinder pressure. The predicted Coefficient of Variation (COV) of cylinder pressure is improved compared to the one captured by closed cycle simulations in parallel.
|Original language||English (US)|
|Title of host publication||SAE Technical Paper Series|
|State||Published - Mar 28 2017|
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This research used resources of the KAUST Supercomputing Laboratory located at King Abdullah University of Science and Technology, Saudi Arabia. Components of this work were supported by the U.S. Department of Energy, Vehicle Technologies Office.