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dc.contributor.authorJaasim, Mohammed
dc.contributor.authorPasunurthi, Shyamsundar
dc.contributor.authorJupudi, Ravichandra S.
dc.contributor.authorGubba, Sreenivasa Rao
dc.contributor.authorPrimus, Roy
dc.contributor.authorKlingbeil, Adam
dc.contributor.authorWijeyakulasuriya, Sameera
dc.contributor.authorIm, Hong G.
dc.date.accessioned2017-05-04T12:33:22Z
dc.date.available2017-05-04T12:33:22Z
dc.date.issued2017-03-13
dc.identifier.urihttp://hdl.handle.net/10754/623336
dc.description.abstractStandard practices of internal combustion (IC) engine experiments are to conduct the measurements of quantities averaged over a large number of cycles. Depending on the operating conditions, the cycle-to-cycle variation (CCV) of quantities, such as the indicated mean effective pressure (IMEP) are observed at different levels. Accurate prediction of CCV in IC engines is an important but challenging task. Computational fluid dynamics (CFD) simulations using high performance computing (HPC) can be used effectively to visualize such 3D spatial distributions. In the present study, a dual fuel large engine is considered, with natural gas injected into the manifold accompanied with direct injection of diesel pilot fuel to trigger ignition. Multiple engine cycles in 3D are simulated in series as in the experiments to investigate the potential of HPC based high fidelity simulations to accurately capture the cycle to cycle variation in dual fuel engines. Open cycle simulations are conducted to predict the combined effect of the stratification of fuel-air mixture, temperature and turbulence on the CCV of pressure. The predicted coefficient of variation (COV) of pressure compared to the results from closed cycle simulations and the experiments.
dc.titleSimulation of Cycle-to-Cycle Variation in Dual-Fuel Engines
dc.typePoster
dc.contributor.departmentClean Combustion Research Center
dc.contributor.departmentComputational Reacting Flow Laboratory (CRFL)
dc.contributor.departmentMechanical Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.conference.dateMarch 13-15, 2017
dc.conference.nameHigh Performance Computing Saudi Arabia (HPC Saudi) 2017
dc.conference.locationKAUST
dc.contributor.institutionConvergent Science Inc
dc.contributor.institutionGE Global Research
kaust.personJaasim, Mohammed
kaust.personIm, Hong G.
refterms.dateFOA2018-06-14T04:49:45Z


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