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Numerical Analysis on the Effect of Piston Bowl Geometry in Gasoline-Diesel Dual-Fuel Combustion
Technical Paper
2019-01-1164
ISSN: 0148-7191, e-ISSN: 2688-3627
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Abstract
As emissions regulations become stricter, a variety of advanced combustion concepts that can reduce emissions with a higher thermal efficiency have been suggested. Dual-fuel combustion is one of the alternatives that has both premixed and non-premixed combustion characteristics. Knowing the effects of the mixture formation in dual-fuel combustion is important because it determines the ignition location and the following combustion phase. Hence, a thorough investigation on the related factors, such as the engine hardware or fuel spray, is required. Meanwhile, Computational Fluid Dynamics (CFD) is a good technique to visualize the in-cylinder phenomena and enables quantitative investigations into the detailed combustion characteristics. In this paper, a 3-dimensional CFD simulation was used to investigate the effects of the mixture formation in dual-fuel combustion. The combustion model consists of two parts. The Representative Interactive Flamelet (RIF) model was used to solve the auto-ignition of the direct injected fuel, and the level-set approach was used to mimic the flame propagation. The reduced Primary Reference Fuel (PRF) mechanism with 73 species and 296 reactions was used. The combustion model was validated with experimental results under gasoline-diesel dual-fuel combustion in a single cylinder diesel engine where the diesel-gasoline ratio was varied. Then, the effects of the mixture formation on the combustion and emissions characteristics were investigated by changing the piston bowl shape. The model showed good agreement with the experimental results, and it could quantitatively analyze how the spray targeting affects the emissions reduction and thermal efficiency.
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Moon, S., Kim, G., Chu, S., Kang, J. et al., "Numerical Analysis on the Effect of Piston Bowl Geometry in Gasoline-Diesel Dual-Fuel Combustion," SAE Technical Paper 2019-01-1164, 2019, https://doi.org/10.4271/2019-01-1164.Data Sets - Support Documents
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