Understanding the physics governing primary atomization of high pressure fuel sprays is of paramount importance to accurately model combustion in direct injection engines. The small length and time scales of features that characterize this process falls below the resolution power of typical grids in CFD simulations, which necessitates the inclusion of physical models (sub-models) to account for unresolved physics. Unfortunately current physical models for fuel spray atomization used in engine CFD simulations are based on significant empirical scaling because there is a lack of experimental data to understand the governing physics. The most widely employed atomization sub-model used in current CFD simulations assumes the spray atomization process to be dominated by aerodynamically-driven surface instabilities, but there has been no quantitative experimental validation of this theory to date. The lack of experimental validation is due to the high spatial and temporal resolutions required to simultaneously to image these instabilities, which is difficult to achieve.
The present work entails the development of a diagnostic technique to obtain high spatial and temporal resolution images of jet breakup and atomization in the near nozzle region of Gasoline Direct Injection (GDI) sprays. It focuses on the optical setup required to achieve maximum illumination, image contrast, sharp feature detection, and temporal tracking of interface instabilities for long-range microscopic imaging with a high-speed camera. The resolution and performance of the imaging system is characterized by evaluating its modulation transfer function (MTF). The setup enabled imaging of GDI sprays for the entire duration of an injection event (several milliseconds) at significantly improved spatial and temporal resolutions compared to historical spray atomization imaging data. The images show that low to moderate injection pressure sprays can be visualized with a high level of detail and also enable the tracking of features across frames within the field of view (FOV)
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/53611 |
| Date | 08 June 2015 |
| Creators | Zaheer, Hussain |
| Contributors | Genzale, Caroline L. |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
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