Cycle to Cycle Variation Study in a Dual Fuel Operated Engine

Shyamsundar Pasunurthi, Ravichandra Jupudi, Sameera Wijeyakulasuriya, Sreenivasa Rao Gubba, Hong Im, Mohammed Jaasim Mubarak Ali, Roy Primus, Adam Klingbeil, Charles Finney

Research output: Contribution to journalConference articlepeer-review

9 Scopus citations

Abstract

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 [1], 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 languageEnglish
JournalSAE Technical Papers
Volume2017-March
Issue numberMarch
DOIs
StatePublished - Mar 28 2017
EventSAE World Congress Experience, WCX 2017 - Detroit, United States
Duration: Apr 4 2017Apr 6 2017

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