A New Euler/Lagrange Approach for Multiphase Simulations of a Multi-Hole GDI Injector RWTH Aachen University

Format:

Conference/Event

Author/Creator:

Bode, Bode, author.

Contributor:

Arima, Toshiyuki

Falkenstein, Tobias

Kang, Seongwon

Le Chenadec, Vincent

Pitsch, Heinz

Taniguchi, Hiroyoshi

Conference Name:

SAE 2015 World Congress & Exhibition (2015-04-21 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2015

Summary:

AbstractCompared to conventional injection techniques, Gasoline Direct Injection (GDI) has a lot of advantages such as increased fuel efficiency, high power output and low emission levels, which can be more accurately controlled. Therefore, this technique is an important topic of today's injection system research.Although the operating conditions of GDI injectors are simpler from a numerical point of view because of smaller Reynolds and Weber numbers compared to Diesel injection systems, accurate simulations of the breakup in the vicinity of the nozzle are very challenging. Combined with the complications of experimental techniques that could be applied inside the nozzle and at the nozzle exit, this is the reason for the lack of understanding the primary breakup behavior of current GDI injectors.In this work, this issue is addressed by combining high-fidelity primary breakup simulations in the vicinity of the nozzle exit, which use the velocity profile at the nozzle exit as boundary condition, and common Lagrange spray simulations. In detail, these enhanced simulations of a 6-hole state-of-the-art GDI injector are compared with the results of fully-Lagrange spray (FLS) simulations and experiments.It is shown that the accuracy of common Lagrange spray simulations can be increased by the combination with high-fidelity primary breakup simulations. This approach avoids the necessity of assuming a droplet size distribution at the nozzle exit, which requires usually tuning by using experimental data. Thus, its potential for a-priori spray predictions, which are for example desired within the injector design process, is much higher

Notes:

Vendor supplied data

Publisher Number:

2015-01-0949

Access Restriction:

Restricted for use by site license