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Technical Paper

Investigation of Cycle-to-Cycle Variation of Turbulent Flow in a High-Tumble SI Engine

2017-10-08
2017-01-2210
The thermal efficiency of a spark-ignition (SI) engine must be improved to reduce both environmental load and fuel consumption. Although lean SI engine operation can strongly improve thermal efficiency relative to that of stoichiometric SI operation, the cycle-to-cycle variation (CCV) of combustion increases with the air dilution level. Combustion CCV is caused by CCVs of many factors, such as EGR, spark energy, air-fuel ratio, and in-cylinder flow structure related to engine speed. This study focuses on flow structures, especially the influence of a tumble structure on flow fluctuation intensity near ignition timing. We measured the flow field at the vertical center cross section of an optically accessible high-tumble flow engine using time-resolved particle image velocimetry. There are many factors considered to be sources of CCV, we analyzed three factors: the intake jet distribution, distribution of vortex core position and trajectory of the fluid particle near the spark plug.
Technical Paper

A Cycle-to-Cycle Variation Extraction Method for Flow Field Analysis in SI IC Engines Based on Turbulence Scales

2019-01-15
2019-01-0042
To adhere to stringent environmental regulations, SI (spark ignition) engines are required to achieve higher thermal efficiency. In recent years, EGR (exhaust gas recirculation) systems and lean-burn operation has been recognized as key technologies. Under such operating conditions, reducing CCV (cycle-to-cycle variation) in combustion is critical to the enhancement of overall engine performance. Flow-field CCV is one of the considerable factors affecting combustion in engines. Conventionally, in research on flow fields in SI engines, the ensemble average is used to separate the measured velocity field into a mean component and a fluctuation component, the latter of which contains a CCV component and a turbulent component. To extract the CCV of the flow field, previous studies employed spatial filter, temporal filter, and POD (proper orthogonal decomposition) methods.
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