Journal
FUEL
Volume 289, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.fuel.2020.119923
Keywords
Cavitation; Erosion; E100 fuel; Gasoline direct injection; LES; URANS
Categories
Funding
- European Union Horizon-2020 Research and Innovation Program [675676]
- FNR [12553319]
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This study presents computational fluid dynamics predictions for multi-hole gasoline direct injection (GDi) injectors using Ethanol (E100), focusing on flow development and cavitation erosion locations, as well as evaluating various erosive indices against durability tests.
Ethanol (E100) can be utilised in spark ignition engines for passenger car vehicles. This brings a challenge to the durability of the fuel injection system components since its use can result in corrosion, further enhanced by cavitation-induced erosion. This work reports computational fluid dynamics (CFD) predictions for both the flow development and the locations prone to cavitation erosion in multi-hole gasoline direct injection (GDi) injectors operated with E100. The compressible form of the Navier-Stokes equations is solved numerically considering the motion of the injector's needle valve. Thermodynamic and mechanical equilibrium is assumed between the liquid, vapour and non-condensable gas; E100 liquid and vapour are considered as a barotropic fluids where the corresponding variation in density with pressure and the speed of sound are estimated via a relevant equation of state; an additional transport equation is solved for simulating the non-condensable air entrainment into the injector during the dwell time between successive injections. Turbulence is modelled using both large eddy simulation (LES) and Unsteady Reynolds-averaged Navier-Stokes (URANS) considering a sector and the full nozzle geometry, respectively. Various cavitation erosion indices reported in the literature are evaluated against new durability tests of surface erosion damage obtained after 400 M injection cycles. The relevant nozzle wall erosion images are found to correlate well with the accumulated erosive power predicted from the computational model.
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